Method, array and use for determining the presence of pancreatic cancer

ABSTRACT

The present invention relates to a method for determining the presence of pancreatic cancer in an individual comprising or consisting of the steps of: (a) providing a sample to be tested from the individual, and (b) determining a biomarker signature of the test sample by measuring the expression in the test sample of one or more biomarkers selected from the group defined in Table III, wherein the expression in the test sample of one or more biomarkers selected from the group defined in Table III is indicative of the individual having pancreatic cancer. The invention also comprises arrays and kits of parts for use in the method of the invention.

This application is a continuation application of U.S. patentapplication Ser. No. 15/684,090, filed Aug. 23, 2017, which is acontinuation application of U.S. patent application Ser. No. 14/002,494,filed Oct. 8, 2013, which is a § 371 application of PCT/GB2012/050483,filed Mar. 5, 2012, which in turn claims priority to GB Application1103726.4, filed Mar. 4, 2011. The entire disclosure of each of theforegoing applications is incorporated by reference herein.

FIELD OF INVENTION

The present invention relates to methods for diagnosis of pancreaticcancer, and biomarkers and arrays for use in the same.

BACKGROUND

Despite major efforts, pancreatic cancer (PaC) still carries a poorprognosis [1]. While PaC is only the 10^(th) most common cancer, it isthe 4^(th) leading cause of cancer death in the USA [2-4]. In fact, the5-year survival is <5%, the lowest of all malignancies [2-3]. However,recent data have shown that the outcome could be dramatically improvedby early detection when the cancer is still predominantly at stage I, asillustrated by a 5-year survival of 30-60% (≤20 mm sized tumour) andeven >75% (≤10 mm sized tumour) after early PaC resection [2-4].

PaC is characterized by a rapid tumour progression, earlymetastasization, and unresponsiveness to most conventional therapies [1,5]. The poor prognosis is mainly due to the lack of effective earlydiagnostics combined with that disease-specific clinical symptoms occurlate in the course of the disease. At the time of diagnosis, the tumourhas often reached a size of 30-40 mm and a majority of all patients(52%) already have metastases, 26% locally advanced cancer, and only 7%have tumours confined to the pancreas [2, 4]. At this time, about 15% ofthe patients are still operable, but their median survival is only 20months.

A variety of non-invasive methodologies, including (endoscopic)ultrasound, computed tomography, and/or endoscopic retrogradecholangio-pancreatography, are used for PaC diagnostics [1-2, 6]. Albeitpowerful, these methods are not specific for PaC and not designed forearly detection when the tumour is still small and potentially curable.The situation is further complicated by the fact that PaC is difficultto differentiate from benign conditions, such as chronic pancreatitis,using currently available diagnostic tools [2].

Hence, the use of biomarkers for specific and early detection of PaCwould be of invaluable clinical benefit.

In spite of major efforts, molecular fingerprints associated with PaCfrom in particular, crude, non-fractionated serum and plasma, remains tobe deciphered [2, 7-9]. Among the number of mainly single biomarkersthat have been outlined so far, including e.g. CRP, CA 242, GDF-15,haptoglobin, M2-pyruvate kinase, serum amyloid A, IGFBP-1, none haveproven to be clinically superior to CA 19-9 [2, 8-10]. Still, the use ofCA 19-9 is significantly hampered by the fact that it has been found toi) be elevated in both non-malignant conditions (e.g. pancreatitis andacute cholangitis) and other gastro-intestinal cancers (e.g. gastriccancer and colorectal cancer), ii) lack sensitivity for early PaC, andiii) be absent in about 10% of the population [2, 8-10]. When screeningfor PaC, CA 19-9 has only yielded medium sensitivity (ranging from 69%to 98%) and specificity (46% to 98%) [2, 9-11].

Against this background, the inventors developed a proteomic approach toprognostic diagnosis of cancer in WO 2008/117067 whereby the first setsof serum biomarkers for detection of pancreatic cancer and forpredicting survival were identified.

SUMMARY OF THE INVENTION

Motivated by a recent study, in which we indicated that affinityproteomics [12-13] could be used to pin-point candidate PaC serumbiomarker signatures [14], we have further deciphered the serum proteomeof PaC.

In this study, we have for the first time pre-validated multiplexedserum biomarker signatures for PaC diagnosis, demonstrating thatdiagnostic information could be extracted from crude blood samples,displaying high specificity and sensitivity. This provides enhanced PaCdiagnosis and thereby improved prognosis, bringing significantly addedclinical value, as well as shedding further light on the underlying,intricate disease biology.

Accordingly, a first aspect of the invention provides a method fordetermining the presence of pancreatic cancer in an individualcomprising or consisting of the steps of:

-   -   a) providing a sample to be tested from the individual;    -   b) determining a biomarker signature of the test sample by        measuring the expression in the test sample of one or more        biomarkers selected from the group defined in Table III;

wherein the expression in the test sample of one or more biomarkersselected from the group defined in Table III is indicative of theindividual having pancreatic cancer.

By “sample to be tested”, “test sample” or “control sample” we includetissue, fluid proteome and/or expressome samples from an individual tobe tested or a control individual, as appropriate.

By “expression” we mean the level or amount of a gene product such asmRNA or protein.

Methods of detecting and/or measuring the concentration of proteinand/or nucleic acid are well known to those skilled in the art, see forexample Sambrook and Russell, 2001, Cold Spring Harbor Laboratory Press.

By “biomarker” we mean a naturally-occurring biological molecule, orcomponent or fragment thereof, the measurement of which can provideinformation useful in the prognosis of pancreatic cancer. For example,the biomarker may be a naturally-occurring protein or carbohydratemoiety, or an antigenic component or fragment thereof.

In one embodiment, the method comprises or consists of steps (a) and (b)and the further steps of:

-   -   c) providing a control sample from an individual not afflicted        with pancreatic cancer (i.e. a negative control);    -   d) determining a biomarker signature of the control sample by        measuring the expression in the control sample of the one or        more biomarkers measured in step (b);

wherein the presence of pancreatic cancer is identified in the eventthat the expression in the test sample of the one or more biomarkersmeasured in step (b) is different from the expression in the controlsample of the one or more biomarkers measured in step (d).

In another embodiment, the method comprises or consists of steps (a),(b), (c) and (d) and the additional steps of:

-   -   e) providing a control sample from an individual afflicted with        pancreatic cancer (i.e. a positive control);    -   f) determining a biomarker signature of the control sample by        measuring the expression in the control sample of the one or        more biomarkers measured in step (b);

wherein the presence of pancreatic cancer is identified in the eventthat the expression in the test sample of the one or more biomarkersmeasured in step (b) corresponds to the expression in the control sampleof the one or more biomarkers measured in step (f).

By “corresponds to the expression in the control sample” we include thatthe expression of the one or more biomarkers in the sample to be testedis the same as or similar to the expression of the one or morebiomarkers of the positive control sample. Preferably the expression ofthe one or more biomarkers in the sample to be tested is identical tothe expression of the one or more biomarkers of the positive controlsample.

Differential expression (up-regulation or down regulation) ofbiomarkers, or lack thereof, can be determined by any suitable meansknown to a skilled person. Differential expression is determined to a pvalue of a least less than 0.05 (p=<0.05), for example, at least <0.04,<0.03, <0.02, <0.01, <0.009, <0.005, <0.001, <0.0001, <0.00001 or atleast <0.000001. Preferably, differential expression is determined usinga support vector machine (SVM). Preferably, the SVM is an SVM asdescribed below. Most preferably, the SVM is the SVM described in TableV(A), below.

It will be appreciated by persons skilled in the art that differentialexpression may relate to a single biomarker or to multiple biomarkersconsidered in combination (i.e. as a biomarker signature). Thus, a pvalue may be associated with a single biomarker or with a group ofbiomarkers. Indeed, proteins having a differential expression p value ofgreater than 0.05 when considered individually may nevertheless still beuseful as biomarkers in accordance with the invention when theirexpression levels are considered in combination with one or more otherbiomarkers.

In one embodiment, step (b) comprises or consists of measuring theexpression of one or more of the biomarkers listed in Table IV(A), forexample, at least 2 of the biomarkers listed in Table IV(A).

As exemplified in the accompanying examples, the expression of certainproteins in a blood, serum or plasma test sample may be indicative ofpancreatic cancer in an individual. For example, the relative expressionof certain serum proteins in a single test sample may be indicative ofthe presence of pancreatic cancer in an individual.

Preferably, the individual is a human. However, the individual beingtested may be any mammal, such as a domesticated mammal (preferably ofagricultural or commercial significance including a horse, pig, cow,sheep, dog and cat).

Preferably, step (b) comprises or consists of measuring the expressionof interleukin-7 (IL-7) and/or integrin alpha-10, for example, measuringthe expression of interleukin-7, measuring the expression of integrinalpha-10, or measuring the expression of interleukin-7 and integrinalpha-10. Most preferably, step (b) comprises or consists of measuringthe expression of each the biomarkers listed in Table IV(A).

In one embodiment, step (b) comprises or consists of measuring theexpression of 1 or more biomarkers from the biomarkers listed in TableIV(B), for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17 or 18 of the biomarkers listed in Table IV(B). Hence, step(b) preferably comprises or consists of measuring the expression of allof the biomarkers listed in Table IV(B).

In another embodiment, step (b) comprises or consists of measuring theexpression of 1 or more biomarkers from the biomarkers listed in TableIV(C), for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32or 33 of the biomarkers listed in Table IV(C). Preferably, step (b)comprises or consists of measuring the expression of all of thebiomarkers listed in Table IV(C).

Preferably, step (b) comprises or consists of measuring the expressionin the test sample of all of the biomarkers defined in Table IV.

In one embodiment, the method is for differentiating between pancreaticcancer (PaC) and another disease state.

Preferably, step (b) comprises or consists of measuring the expressionin the test sample of 1 or more biomarkers from the biomarkers listed inTable V(A), for example at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 of thebiomarkers listed in Table V(A). Preferably, step (b) also comprises orconsists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(B), for example atleast 2, 3, 4, 5, 6, 7, 8, 9 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23 or 24 of the biomarkers listed in Table V(B). It is alsopreferred that step (b) comprises or consists of measuring theexpression in the test sample of 1 or more biomarkers from thebiomarkers listed in Table V(C), for example at least 2, 3, 4 or 5 ofthe biomarkers listed in Table V(C). Preferably, step (b) comprises orconsists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(D), for example atleast 2 or 3 of the biomarkers listed in Table V(D). Preferably, step(b) comprises or consists of measuring the expression in the test sampleof 1 or more biomarkers from the biomarkers listed in Table V(F), forexample at least 2, 3, 4, 5 or 6 of the biomarkers listed in Table V(F).Preferably, step (b) comprises or consists of measuring the expressionin the test sample of all of the biomarkers listed in Table V(A), TableV(B), Table V(C), Table V(D) and/or Table V(F).

By “differentiating between pancreatic cancer (PaC) and another diseasestate” we include differentiating between PaC and any other condition,including a state of health.

In one embodiment, the other disease state or states is chronicpancreatitis (ChP), acute inflammatory pancreatitis (AIP) and/or normal,for example, the other disease state or states may be chronicpancreatitis alone; acute inflammatory pancreatitis alone; chronicpancreatitis and acute inflammatory pancreatitis; chronic pancreatitisand normal; acute inflammatory pancreatitis and normal; or, chronicpancreatitis, acute inflammatory pancreatitis and normal.

When referring to a “normal” disease state we include individuals notafflicted with chronic pancreatitis (ChP) or acute inflammatorypancreatitis (AIP). Preferably the individuals are not afflicted withany pancreatic disease or disorder. Most preferably, the individuals arehealthy individuals, i.e., they are not afflicted with any disease ordisorder.

In a another embodiment, the method is for differentiating betweenpancreatic cancer and chronic pancreatitis (ChP). Preferably, step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(A), forexample at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers listedin Table V(A). Step (b) may comprise or consist of measuring theexpression in the test sample of 1 or more biomarkers from thebiomarkers listed in Table V(C), for example at least 2, 3, 4 or 5 ofthe biomarkers listed in Table V(C). Step (b) may comprise or consist ofmeasuring the expression in the test sample of all of the biomarkerslisted in Table V(A) and/or Table V(C).

In an additional/alternative embodiment, the method is fordifferentiating between pancreatic cancer and acute inflammatorypancreatitis (AIP) and step (b) comprises or consists of measuring theexpression in the test sample of 1 or more biomarkers from thebiomarkers listed in Table V(A), for example at least 2, 3, 4, 5, 6, 7,8, 9 or 10 of the biomarkers listed in Table V(A). Preferably, step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(B), forexample at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23 or 24 of the biomarkers listed in Table V(B).Step (b) may comprise or consist of measuring the expression in the testsample of 1 or more biomarkers from the biomarkers listed in Table V(C),for example at least 2, 3, 4 or 5 of the biomarkers listed in TableV(C). Preferably, step (b) comprises or consists of measuring theexpression in the test sample of 1 or more biomarkers from thebiomarkers listed in Table V(E). Preferably, step (b) comprises orconsists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(F), for example atleast 2, 3, 4, 5 or 6 of the biomarkers listed in Table V(F).Preferably, step (b) comprises or consists of measuring the expressionin the test sample of 1 or more biomarkers from the biomarkers listed inTable V(H), for example at least 2 or 3 of the biomarkers listed inTable V(H). Hence, step (b) preferably comprises or consists ofmeasuring the expression in the test sample of all of the biomarkerslisted in Table V(A), Table V(B), Table V(C), Table V(E), Table V(F)and/or Table IV(H).

In one embodiment, the method is for differentiating between pancreaticcancer and normal (N). For a definition of “normal” disease state, seeabove. Preferably, step (b) comprises or consists of measuring theexpression in the test sample of 1 or more biomarkers from thebiomarkers listed in Table V(A), for example at least 2, 3, 4, 5, 6, 7,8, 9 or 10 of the biomarkers listed in Table V(A). Preferably, step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(B), forexample at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23 or 24 of the biomarkers listed in Table V(B).Preferably, step (b) comprises or consists of measuring the expressionin the test sample of 1 or more biomarkers from the biomarkers listed inTable V(D), for example at least 2 or 3 of the biomarkers listed inTable V(D). Preferably, wherein step (b) comprises or consists ofmeasuring the expression in the test sample of 1 or more biomarkers fromthe biomarkers listed in Table V(E). It is also preferred that step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(G), forexample at least 2 or 3 of the biomarkers listed in Table V(G). Hence,step (b) may comprise or consist of measuring the expression in the testsample of all of the biomarkers listed in Table V(A), Table V(B), TableV(D), Table V(E) and/or Table IV(G).

In one embodiment, step (b) comprises or consists of measuring theexpression of IL-3. In a further embodiment, step (b) comprises orconsists of measuring the expression of Integrin α-10. In a stillfurther embodiment, step (b) comprises or consists of measuring theexpression of Mucin-1. In another embodiment, step (b) comprises orconsists of measuring the expression of C1s. In an additionalembodiment, step (b) comprises or consists of measuring the expressionof MCP-3. In one embodiment, step (b) comprises or consists of measuringthe expression of Angiomotin. In a further embodiment, step (b)comprises or consists of measuring the expression of BTK. In a stillfurther embodiment, step (b) comprises or consists of measuring theexpression of C1q. In another embodiment, step (b) comprises or consistsof measuring the expression of CD40 ligand.

In an additional embodiment, step (b) comprises or consists of measuringthe expression of GM-CSF. In one embodiment, step (b) comprises orconsists of measuring the expression of IgM. In a further embodiment,step (b) comprises or consists of measuring the expression of IL-11. Ina still further embodiment, step (b) comprises or consists of measuringthe expression of IL-16. In another embodiment, step (b) comprises orconsists of measuring the expression of IL-1-ra. In an additionalembodiment, step (b) comprises or consists of measuring the expressionof IL-1α. In one embodiment, step (b) comprises or consists of measuringthe expression of IL-1β. In a further embodiment, step (b) comprises orconsists of measuring the expression of IL-2. In a still furtherembodiment, step (b) comprises or consists of measuring the expressionof IL-7. In another embodiment, step (b) comprises or consists ofmeasuring the expression of IL-9. In an additional embodiment, step (b)comprises or consists of measuring the expression of INF-γ. In oneembodiment, step (b) comprises or consists of measuring the expressionof Integrin α-11. In a further embodiment, step (b) comprises orconsists of measuring the expression of JAK3. In a still furtherembodiment, step (b) comprises or consists of measuring the expressionof Leptin. In another embodiment, step (b) comprises or consists ofmeasuring the expression of Lewis y. In an additional embodiment, step(b) comprises or consists of measuring the expression of MCP-4. In oneembodiment, step (b) comprises or consists of measuring the expressionof Procathepsin W. In a further embodiment, step (b) comprises orconsists of measuring the expression of Properdin. In a still furtherembodiment, step (b) comprises or consists of measuring the expressionof PSA. In another embodiment, step (b) comprises or consists ofmeasuring the expression of RANTES. In an additional embodiment, step(b) comprises or consists of measuring the expression of Sialyl Lewis x.In one embodiment, step (b) comprises or consists of measuring theexpression of TM peptide. In a further embodiment, step (b) comprises orconsists of measuring the expression of TNF-α. In a still furtherembodiment, step (b) comprises or consists of measuring the expressionof C4. In another embodiment, step (b) comprises or consists ofmeasuring the expression of β-galactosidase.

In an additional embodiment, step (b) comprises or consists of measuringthe expression of IL-12. In one embodiment, step (b) comprises orconsists of measuring the expression of TGF-61. In a further embodiment,step (b) comprises or consists of measuring the expression of VEGF. In astill further embodiment, step (b) comprises or consists of measuringthe expression of IL-8. In another embodiment, step (b) comprises orconsists of measuring the expression of C3. In an additional embodiment,step (b) comprises or consists of measuring the expression of IFN-γ. Inone embodiment, step (b) comprises or consists of measuring theexpression of IL-10. In a further embodiment, step (b) comprises orconsists of measuring the expression of IL-13. In a still furtherembodiment, step (b) comprises or consists of measuring the expressionof IL-18. In another embodiment, step (b) comprises or consists ofmeasuring the expression of IL-6. In an additional embodiment, step (b)comprises or consists of measuring the expression of Lewis x. In oneembodiment, step (b) comprises or consists of measuring the expressionof Eotaxin. In a further embodiment, step (b) comprises or consists ofmeasuring the expression of C1 esterase inhibitor. In a still furtherembodiment, step (b) comprises or consists of measuring the expressionof MCP-1. In another embodiment, step (b) comprises or consists ofmeasuring the expression of TNF-β. In an additional embodiment, step (b)comprises or consists of measuring the expression of GLP-1. In oneembodiment, step (b) comprises or consists of measuring the expressionof IL-5. In a further embodiment, step (b) comprises or consists ofmeasuring the expression of IL-4. In a still further embodiment, step(b) comprises or consists of measuring the expression of Factor B. Inanother embodiment, step (b) comprises or consists of measuring theexpression of C5. In an additional embodiment, step (b) comprises orconsists of measuring the expression of CD40.

In one embodiment, step (b) does not comprise measuring the expressionof IL-3. In a further embodiment, step (b) does not comprise measuringthe expression of Integrin α-10. In a still further embodiment, step (b)does not comprise measuring the expression of Mucin-1. In anotherembodiment, step (b) does not comprise measuring the expression of C1s.In an additional embodiment, step (b) does not comprise measuring theexpression of MCP-3. In one embodiment, step (b) does not comprisemeasuring the expression of Angiomotin. In a further embodiment, step(b) does not comprise measuring the expression of BTK. In a stillfurther embodiment, step (b) does not comprise measuring the expressionof C1q. In another embodiment, step (b) does not comprise measuring theexpression of CD40 ligand. In an additional embodiment, step (b) doesnot comprise measuring the expression of GM-CSF. In one embodiment, step(b) does not comprise measuring the expression of IgM. In a furtherembodiment, step (b) does not comprise measuring the expression ofIL-11. In a still further embodiment, step (b) does not comprisemeasuring the expression of IL-16. In another embodiment, step (b) doesnot comprise measuring the expression of IL-1-ra. In an additionalembodiment, step (b) does not comprise measuring the expression ofIL-1α. In one embodiment, step (b) does not comprise measuring theexpression of IL-1β. In a further embodiment, step (b) does not comprisemeasuring the expression of IL-2. In a still further embodiment, step(b) does not comprise measuring the expression of IL-7. In anotherembodiment, step (b) does not comprise measuring the expression of IL-9.In an additional embodiment, step (b) does not comprise measuring theexpression of INF-γ. In one embodiment, step (b) does not comprisemeasuring the expression of Integrin α-11. In a further embodiment, step(b) does not comprise measuring the expression of JAK3. In a stillfurther embodiment, step (b) does not comprise measuring the expressionof Leptin. In another embodiment, step (b) does not comprise measuringthe expression of Lewis y. In an additional embodiment, step (b) doesnot comprise measuring the expression of MCP-4. In one embodiment, step(b) does not comprise measuring the expression of Procathepsin W. In afurther embodiment, step (b) does not comprise measuring the expressionof Properdin. In a still further embodiment, step (b) does not comprisemeasuring the expression of PSA. In another embodiment, step (b) doesnot comprise measuring the expression of RANTES. In an additionalembodiment, step (b) does not comprise measuring the expression ofSialyl Lewis x. In one embodiment, step (b) does not comprise measuringthe expression of TM peptide. In a further embodiment, step (b) does notcomprise measuring the expression of TNF-α. In a still furtherembodiment, step (b) does not comprise measuring the expression of C4.In another embodiment, step (b) does not comprise measuring theexpression of β-galactosidase.

In an additional embodiment, step (b) does not comprise measuring theexpression of IL-12. In one embodiment, step (b) does not comprisemeasuring the expression of TGF-β1. In a further embodiment, step (b)does not comprise measuring the expression of VEGF. In a still furtherembodiment, step (b) does not comprise measuring the expression of IL-8.In another embodiment, step (b) does not comprise measuring theexpression of C3. In an additional embodiment, step (b) does notcomprise measuring the expression of IFN-γ. In one embodiment, step (b)does not comprise measuring the expression of IL-10. In a furtherembodiment, step (b) does not comprise measuring the expression ofIL-13. In a still further embodiment, step (b) does not comprisemeasuring the expression of IL-18. In another embodiment, step (b) doesnot comprise measuring the expression of IL-6. In an additionalembodiment, step (b) does not comprise measuring the expression of Lewisx. In one embodiment, step (b) does not comprise measuring theexpression of Eotaxin. In a further embodiment, step (b) does notcomprise measuring the expression of C1 esterase inhibitor. In a stillfurther embodiment, step (b) does not comprise measuring the expressionof MCP-1. In another embodiment, step (b) does not comprise measuringthe expression of TNF-β. In an additional embodiment, step (b) does notcomprise measuring the expression of GLP-1. In one embodiment, step (b)does not comprise measuring the expression of IL-5. In a furtherembodiment, step (b) does not comprise measuring the expression of IL-4.In a still further embodiment, step (b) does not comprise measuring theexpression of Factor B. In another embodiment, step (b) does notcomprise measuring the expression of C5. In an additional embodiment,step (b) does not comprise measuring the expression of CD40.

By “TM peptide” we mean a peptide derived from a 10TM protein, to whichthe scFv antibody construct of SEQ ID NO: 1 below has specificity(wherein the CDR sequences are indicated by bold, italicised text):

[SEQ ID NO: 1] MAEVQLLESGGGLVQPGGSLRLSCAASGFT

KGLEWV

FTISRDNSKNTLYLQMNSLRAEDTAVYYCARGTWFDPWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCS

WYQ QLPGTAPKLLIY

GVPDRFSGSKSGTSASLAISGLRSEDEADYY

FGGGTKLTVLG

Hence, this scFv may be used or any antibody, or antigen bindingfragment thereof, that competes with this scFv for binding to the 10TMprotein. For example, the antibody, or antigen binding fragment thereof,may comprise the same CDRs as present in SEQ ID NO:1.

It will be appreciated by persons skilled in the art that such anantibody may be produced with an affinity tag (e.g. at the C-terminus)for purification purposes. For example, an affinity tag of SEQ ID NO: 2below may be utilised:

[SEQ ID NO: 2] DYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH

In one embodiment, presence of pancreatic cancer is identified in theevent that the expression in the test sample of IL-3, Integrinα-10,Mucin-1, C1s, GLP-1R, MCP-3, Angiomotin, BTK, CD40 ligand, GM-CSF, IgM,IL-11, IL-16, IL-1-ra, IL-la, IL-1β, IL-2, IL-7, IL-9, INF-γ,Integrinα-11, JAK3, Leptin, Lewis y, MCP-4, Procathepsin W, PSA, RANTES,Sialyl Lewis x, TM peptide, TNF-α, C4, β-galactosidase, IL-12, TGF-β1,VEGF, IL-8, C3, IFN-γ, IL-10, IL-13, IL-18, IL-6, Lewis x, Eotaxin, C1esterase inhibitor, MCP-1, TNF-β, GLP-1, IL-5, IL-4, Factor B, C5 and/orCD40 are up-regulated compared to the negative control(s) and/orcorresponds to the expression of positive control(s).

In another embodiment, presence of pancreatic cancer is identified inthe event that the expression in the test sample of C1q and/or Properdinis down-regulated compared to the negative control(s) and/or correspondsto the expression of positive control(s) Generally, diagnosis is madewith an ROC AUC of at least 0.55, for example with an ROC AUC of atleast, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 0.96, 0.97, 0.98,0.99 or with an ROC AUC of 1.00. Preferably, diagnosis is made with anROC AUC of at least 0.85, and most preferably with an ROC AUC of 1.

Typically, diagnosis is performed using a support vector machine (SVM),such as those available fromhttp://cran.r-project.org/web/packages/e1071/index.html (e.g. e10711.5-24). However, any other suitable means may also be used.

Support vector machines (SVMs) are a set of related supervised learningmethods used for classification and regression. Given a set of trainingexamples, each marked as belonging to one of two categories, an SVMtraining algorithm builds a model that predicts whether a new examplefalls into one category or the other. Intuitively, an SVM model is arepresentation of the examples as points in space, mapped so that theexamples of the separate categories are divided by a clear gap that isas wide as possible. New examples are then mapped into that same spaceand predicted to belong to a category based on which side of the gapthey fall on.

More formally, a support vector machine constructs a hyperplane or setof hyperplanes in a high or infinite dimensional space, which can beused for classification, regression or other tasks. Intuitively, a goodseparation is achieved by the hyperplane that has the largest distanceto the nearest training datapoints of any class (so-called functionalmargin), since in general the larger the margin the lower thegeneralization error of the classifier. For more information on SVMs,see for example, Burges, 1998, Data Mining and Knowledge Discovery,2:121-167.

In one embodiment of the invention, the SVM is ‘trained’ prior toperforming the methods of the invention using biomarker profiles fromindividuals with known disease status (for example, individuals known tohave pancreatic cancer, individuals known to have acute inflammatorypancreatitis, individuals known to have chronic pancreatitis orindividuals known to be healthy). By running such training samples, theSVM is able to learn what biomarker profiles are associated withpancreatic cancer. Once the training process is complete, the SVM isthen able whether or not the biomarker sample tested is from anindividual with pancreatic cancer.

However, this training procedure can be by-passed by pre-programming theSVM with the necessary training parameters. For example, diagnoses canbe performed according to the known SVM parameters using the SVMalgorithm detailed in Table V, based on the measurement of any or all ofthe biomarkers listed in Table III or Table IV.

It will be appreciated by skilled persons that suitable SVM parameterscan be determined for any combination of the biomarkers listed in TableIII or Table IV by training an SVM machine with the appropriateselection of data (i.e. biomarker measurements from individuals withknown pancreatic cancer status).

Preferably, the method of the invention has an accuracy of at least 60%,for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%,73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%,87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%accuracy.

Preferably, the method of the invention has a sensitivity of at least60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%,72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or100% sensitivity.

Preferably, the method of the invention has a specificity of at least60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%,72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%,86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or100% specificity.

By “accuracy” we mean the proportion of correct outcomes of a method, by“sensitivity” we mean the proportion of all PaC positive sample that arecorrectly classified as positives, and by “specificity” we mean theproportion of all PaC negative samples that are correctly classified asnegatives.

In one embodiment, the individual not afflicted with pancreatic canceris not afflicted with pancreatic cancer (PaC), chronic pancreatitis(ChP) or acute inflammatory pancreatitis (AIP). More preferably, thehealthy individual is not afflicted with any pancreatic disease orcondition. Even more preferably, the individual not afflicted withpancreatic cancer is not afflicted with any disease or condition. Mostpreferably, the individual not afflicted with pancreatic cancer is ahealthy individual. By a “healthy individual” we include individualsconsidered by a skilled person to be physically vigorous and free fromphysical disease.

However, in another embodiment the individual not afflicted withpancreatic cancer is afflicted with chronic pancreatitis. In stillanother embodiment, the individual not afflicted with pancreatic canceris afflicted with acute inflammatory pancreatitis.

As previously mentioned the present method is for determining thepresence of pancreatic cancer in an individual. In one embodiment thepancreatic cancer is selected from the group consisting ofadenocarcinoma, adenosquamous carcinoma, signet ring cell carcinoma,hepatoid carcinoma, colloid carcinoma, undifferentiated carcinoma, andundifferentiated carcinomas with osteoclast-like giant cells.Preferably, the pancreatic cancer is a pancreatic adenocarcinoma. Morepreferably, the pancreatic cancer is pancreatic ductal adenocarcinoma,also known as exocrine pancreatic cancer.

In a further embodiment, step (b), (d) and/or step (f) is performedusing a first binding agent capable of binding to the one or morebiomarkers. It will be appreciated by persons skilled in the art thatthe first binding agent may comprise or consist of a single species withspecificity for one of the protein biomarkers or a plurality ofdifferent species, each with specificity for a different proteinbiomarker.

Suitable binding agents (also referred to as binding molecules) can beselected from a library, based on their ability to bind a given motif,as discussed below.

At least one type of the binding agents, and more typically all of thetypes, may comprise or consist of an antibody or antigen-bindingfragment of the same, or a variant thereof.

Methods for the production and use of antibodies are well known in theart, for example see Antibodies: A Laboratory Manual, 1988, Harlow &Lane, Cold Spring Harbor Press, ISBN-13: 978-0879693145, UsingAntibodies: A Laboratory Manual, 1998, Harlow & Lane, Cold Spring HarborPress, ISBN-13: 978-0879695446 and Making and Using Antibodies: APractical Handbook, 2006, Howard & Kaser, CRC Press, ISBN-13:978-0849335280 (the disclosures of which are incorporated herein byreference).

Thus, a fragment may contain one or more of the variable heavy (V_(H))or variable light (V_(L)) domains. For example, the term antibodyfragment includes Fab-like molecules (Better et al (1988) Science 240,1041); Fv molecules (Skerra et al (1988) Science 240, 1038);single-chain Fv (ScFv) molecules where the V_(H) and V_(L) partnerdomains are linked via a flexible oligopeptide (Bird et al (1988)Science 242, 423; Huston et al (1988) Proc. Natl. Acad. Sci. USA 85,5879) and single domain antibodies (dAbs) comprising isolated V domains(Ward et al (1989) Nature 341, 544).

The term “antibody variant” includes any synthetic antibodies,recombinant antibodies or antibody hybrids, such as but not limited to,a single-chain antibody molecule produced by phage-display ofimmunoglobulin light and/or heavy chain variable and/or constantregions, or other immunointeractive molecule capable of binding to anantigen in an immunoassay format that is known to those skilled in theart.

A general review of the techniques involved in the synthesis of antibodyfragments which retain their specific binding sites is to be found inWinter & Milstein (1991) Nature 349, 293-299.

Molecular libraries such as antibody libraries (Clackson et al, 1991,Nature 352, 624-628; Marks et al, 1991, J Mol Biol 222(3): 581-97),peptide libraries (Smith, 1985, Science 228(4705): 1315-7), expressedcDNA libraries (Santi et al (2000) J Mol Biol 296(2): 497-508),libraries on other scaffolds than the antibody framework such asaffibodies (Gunneriusson et al, 1999, Appl Environ Microbiol 65(9):4134-40) or libraries based on aptamers (Kenan et al, 1999, Methods MolBiol 118, 217-31) may be used as a source from which binding moleculesthat are specific for a given motif are selected for use in the methodsof the invention.

The molecular libraries may be expressed in vivo in prokaryotic(Clackson et al, 1991, op. cit.; Marks et al, 1991, op. cit.) oreukaryotic cells (Kieke et al, 1999, Proc Natl Acad Sci USA,96(10):5651-6) or may be expressed in vitro without involvement of cells(Hanes & Pluckthun, 1997, Proc Natl Acad Sci USA 94(10):4937-42; He &Taussig, 1997, Nucleic Acids Res 25(24):5132-4; Nemoto et al, 1997, FEBSLett, 414(2):405-8).

In cases when protein based libraries are used often the genes encodingthe libraries of potential binding molecules are packaged in viruses andthe potential binding molecule is displayed at the surface of the virus(Clackson et al, 1991, op. cit.; Marks et al, 1991, op. cit; Smith,1985, op. cit.).

The most commonly used such system today is filamentous bacteriophagedisplaying antibody fragments at their surfaces, the antibody fragmentsbeing expressed as a fusion to the minor coat protein of thebacteriophage (Clackson et al, 1991, op. cit.; Marks et al, 1991, op.cit). However, also other systems for display using other viruses (EP39578), bacteria (Gunneriusson et al, 1999, op. cit.; Daugherty et al,1998, Protein Eng 11(9):825-32; Daugherty et al, 1999, Protein Eng12(7):613-21), and yeast (Shusta et al, 1999, J Mol Biol 292(5):949-56)have been used.

In addition, display systems have been developed utilising linkage ofthe polypeptide product to its encoding mRNA in so called ribosomedisplay systems (Hanes & Pluckthun, 1997, op. cit.; He & Taussig, 1997,op. cit.; Nemoto et al, 1997, op. cit.), or alternatively linkage of thepolypeptide product to the encoding DNA (see U.S. Pat. No. 5,856,090 andWO 98/37186).

When potential binding molecules are selected from libraries one or afew selector peptides having defined motifs are usually employed. Aminoacid residues that provide structure, decreasing flexibility in thepeptide or charged, polar or hydrophobic side chains allowinginteraction with the binding molecule may be used in the design ofmotifs for selector peptides.

For example:

-   -   (i) Proline may stabilise a peptide structure as its side chain        is bound both to the alpha carbon as well as the nitrogen;    -   (ii) Phenylalanine, tyrosine and tryptophan have aromatic side        chains and are highly hydrophobic, whereas leucine and        isoleucine have aliphatic side chains and are also hydrophobic;    -   (iii) Lysine, arginine and histidine have basic side chains and        will be positively charged at neutral pH, whereas aspartate and        glutamate have acidic side chains and will be negatively charged        at neutral pH;    -   (iv) Asparagine and glutamine are neutral at neutral pH but        contain a amide group which may participate in hydrogen bonds;    -   (v) Serine, threonine and tyrosine side chains contain hydroxyl        groups, which may participate in hydrogen bonds.

Typically, selection of binding agents may involve the use of arraytechnologies and systems to analyse binding to spots corresponding totypes of binding molecules.

In one embodiment, the first binding agent(s) is/are immobilised on asurface (e.g. on a multiwell plate or array).

The variable heavy (V_(H)) and variable light (V_(L)) domains of theantibody are involved in antigen recognition, a fact first recognised byearly protease digestion experiments. Further confirmation was found by“humanisation” of rodent antibodies. Variable domains of rodent originmay be fused to constant domains of human origin such that the resultantantibody retains the antigenic specificity of the rodent parentedantibody (Morrison et al (1984) Proc. Natl. Acad. Sci. USA 81,6851-6855).

That antigenic specificity is conferred by variable domains and isindependent of the constant domains is known from experiments involvingthe bacterial expression of antibody fragments, all containing one ormore variable domains. These molecules include Fab-like molecules(Better et al (1988) Science 240, 1041); Fv molecules (Skerra et al(1988) Science 240, 1038); single-chain Fv (ScFv) molecules where theV_(H) and V_(L) partner domains are linked via a flexible oligopeptide(Bird et al (1988) Science 242, 423; Huston et al (1988) Proc. Natl.Acad. Sci. USA 85, 5879) and single domain antibodies (dAbs) comprisingisolated V domains (Ward et al (1989) Nature 341, 544). A general reviewof the techniques involved in the synthesis of antibody fragments whichretain their specific binding sites is to be found in Winter & Milstein(1991) Nature 349, 293-299.

By “ScFv molecules” we mean molecules wherein the V_(H) and V_(L)partner domains are linked via a flexible oligopeptide.

The advantages of using antibody fragments, rather than wholeantibodies, are several-fold. The smaller size of the fragments may leadto improved pharmacological properties, such as better penetration ofsolid tissue. Effector functions of whole antibodies, such as complementbinding, are removed. Fab, Fv, ScFv and dAb antibody fragments can allbe expressed in and secreted from E. coli, thus allowing the facileproduction of large amounts of the said fragments.

Whole antibodies, and F(ab′)2 fragments are “bivalent”. By “bivalent” wemean that the said antibodies and F(ab′)2 fragments have two antigencombining sites. In contrast, Fab, Fv, ScFv and dAb fragments aremonovalent, having only one antigen combining sites.

The antibodies may be monoclonal or polyclonal. Suitable monoclonalantibodies may be prepared by known techniques, for example thosedisclosed in “Monoclonal Antibodies: A manual of techniques”, H Zola(CRC Press, 1988) and in “Monoclonal Hybridoma Antibodies: Techniquesand applications”, J G R Hurrell (CRC Press, 1982), both of which areincorporated herein by reference.

In one embodiment, the first binding agent immobilised on a surface(e.g. on a multiwell plate or array).

The variable heavy (V_(H)) and variable light (V_(L)) domains of theantibody are involved in antigen recognition, a fact first recognised byearly protease digestion experiments. Further confirmation was found by“humanisation” of rodent antibodies. Variable domains of rodent originmay be fused to constant domains of human origin such that the resultantantibody retains the antigenic specificity of the rodent parentedantibody (Morrison et al (1984) Proc. Natl. Acad. Sci. USA 81,6851-6855).

That antigenic specificity is conferred by variable domains and isindependent of the constant domains is known from experiments involvingthe bacterial expression of antibody fragments, all containing one ormore variable domains. These molecules include Fab-like molecules(Better et al (1988) Science 240, 1041); Fv molecules (Skerra et al(1988) Science 240, 1038); single-chain Fv (ScFv) molecules where theV_(H) and V_(L) partner domains are linked via a flexible oligopeptide(Bird et al (1988) Science 242, 423; Huston et al (1988) Proc. Natl.Acad. Sci. USA 85, 5879) and single domain antibodies (dAbs) comprisingisolated V domains (Ward et al (1989) Nature 341, 544). A general reviewof the techniques involved in the synthesis of antibody fragments whichretain their specific binding sites is to be found in Winter & Milstein(1991) Nature 349, 293-299.

By “ScFv molecules” we mean molecules wherein the V_(H) and V_(L)partner domains are linked via a flexible oligopeptide.

The advantages of using antibody fragments, rather than wholeantibodies, are several-fold. The smaller size of the fragments may leadto improved pharmacological properties, such as better penetration ofsolid tissue. Effector functions of whole antibodies, such as complementbinding, are removed. Fab, Fv, ScFv and dAb antibody fragments can allbe expressed in and secreted from E. coli, thus allowing the facileproduction of large amounts of the said fragments.

Whole antibodies, and F(ab′)2 fragments are “bivalent”. By “bivalent” wemean that the said antibodies and F(ab′)2 fragments have two antigencombining sites. In contrast, Fab, Fv, ScFv and dAb fragments aremonovalent, having only one antigen combining sites.

The antibodies may be monoclonal or polyclonal. Suitable monoclonalantibodies may be prepared by known techniques, for example thosedisclosed in “Monoclonal Antibodies: A manual of techniques”, H Zola(CRC Press, 1988) and in “Monoclonal Hybridoma Antibodies: Techniquesand applications”, J G R Hurrell (CRC Press, 1982), both of which areincorporated herein by reference.

Hence, the first binding agent may comprise or consist of an antibody oran antigen-binding fragment thereof. Preferably, the antibody orantigen-binding fragment thereof is a recombinant antibody orantigen-binding fragment thereof. The antibody or antigen-bindingfragment thereof may be selected from the group consisting of: scFv,Fab, and a binding domain of an immunoglobulin molecule.

Preferably, the first binding agent is immobilised on a surface.

The one or more biomarkers in the test sample may be labelled with adetectable moiety.

By a “detectable moiety” we include the meaning that the moiety is onewhich may be detected and the relative amount and/or location of themoiety (for example, the location on an array) determined.

Suitable detectable moieties are well known in the art.

Thus, the detectable moiety may be a fluorescent and/or luminescentand/or chemiluminescent moiety which, when exposed to specificconditions, may be detected.

For example, a fluorescent moiety may need to be exposed to radiation(i.e. light) at a specific wavelength and intensity to cause excitationof the fluorescent moiety, thereby enabling it to emit detectablefluorescence at a specific wavelength that may be detected.

Alternatively, the detectable moiety may be an enzyme which is capableof converting a (preferably undetectable) substrate into a detectableproduct that can be visualised and/or detected. Examples of suitableenzymes are discussed in more detail below in relation to, for example,ELISA assays.

Alternatively, the detectable moiety may be a radioactive atom which isuseful in imaging. Suitable radioactive atoms include ^(99m)Tc and ¹²³Ifor scintigraphic studies. Other readily detectable moieties include,for example, spin labels for magnetic resonance imaging (MRI) such as¹²³I again, ¹³¹I, ¹¹¹In, ¹⁹F, ¹³ C, ¹⁵ N, ¹⁷O, gadolinium, manganese oriron. Clearly, the agent to be detected (such as, for example, the oneor more biomarkers in the test sample and/or control sample describedherein and/or an antibody molecule for use in detecting a selectedprotein) must have sufficient of the appropriate atomic isotopes inorder for the detectable moiety to be readily detectable.

The radio- or other labels may be incorporated into the agents of theinvention (i.e. the proteins present in the samples of the methods ofthe invention and/or the binding agents of the invention) in known ways.For example, if the binding moiety is a polypeptide it may bebiosynthesised or may be synthesised by chemical amino acid synthesisusing suitable amino acid precursors involving, for example, fluorine-19in place of hydrogen. Labels such as ^(99m)Tc, ¹²³I, ¹⁸⁶Rh, ¹⁸⁸Rh and¹¹¹In can, for example, be attached via cysteine residues in the bindingmoiety. Yttrium-90 can be attached via a lysine residue. The IODOGENmethod (Fraker et al (1978) Biochem. Biophys. Res. Comm. 80, 49-57) canbe used to incorporate ¹²³I. Reference (“Monoclonal Antibodies inImmunoscintigraphy”, J-F Chatal, CRC Press, 1989) describes othermethods in detail. Methods for conjugating other detectable moieties(such as enzymatic, fluorescent, luminescent, chemiluminescent orradioactive moieties) to proteins are well known in the art.

Preferably, the one or more biomarkers in the control sample(s) arelabelled with a detectable moiety. The detectable moiety may be selectedfrom the group consisting of: a fluorescent moiety; a luminescentmoiety; a chemiluminescent moiety; a radioactive moiety; an enzymaticmoiety. However, it is preferred that the detectable moiety is biotin.

In an additional embodiment step (b), (d) and/or step (f) is performedusing an assay comprising a second binding agent capable of binding tothe one or more biomarkers, the second binding agent comprising adetectable moiety. Preferably, the second binding agent comprises orconsists of an antibody or an antigen-binding fragment thereof.Preferably, the antibody or antigen-binding fragment thereof is arecombinant antibody or antigen-binding fragment thereof. Mostpreferably, the antibody or antigen-binding fragment thereof is selectedfrom the group consisting of: scFv, Fab and a binding domain of animmunoglobulin molecule. In one embodiment the detectable moiety isselected from the group consisting of: a fluorescent moiety; aluminescent moiety; a chemiluminescent moiety; a radioactive moiety andan enzymatic moiety. Preferably, the detectable moiety is fluorescentmoiety (for example an Alexa Fluor dye, e.g. Alexa647).

In one embodiment, the method of the first aspect of the inventioncomprises or consists of an ELISA (Enzyme Linked Immunosorbent Assay).

Preferred assays for detecting serum or plasma proteins include enzymelinked immunosorbent assays (ELISA), radioimmunoassay (RIA),immunoradiometric assays (IRMA) and immunoenzymatic assays (IEMA),including sandwich assays using monoclonal and/or polyclonal antibodies.Exemplary sandwich assays are described by David et al in U.S. Pat. Nos.4,376,110 and 4,486,530, hereby incorporated by reference. Antibodystaining of cells on slides may be used in methods well known incytology laboratory diagnostic tests, as well known to those skilled inthe art.

Typically, the assay is an ELISA (Enzyme Linked Immunosorbent Assay)which typically involves the use of enzymes giving a coloured reactionproduct, usually in solid phase assays. Enzymes such as horseradishperoxidase and phosphatase have been widely employed. A way ofamplifying the phosphatase reaction is to use NADP as a substrate togenerate NAD which now acts as a coenzyme for a second enzyme system.Pyrophosphatase from Escherichia coli provides a good conjugate becausethe enzyme is not present in tissues, is stable and gives a goodreaction colour. Chemi-luminescent systems based on enzymes such asluciferase can also be used.

ELISA methods are well known in the art, for example see The ELISAGuidebook (Methods in Molecular Biology), 2000, Crowther, Humana Press,ISBN-13: 978-0896037281 (the disclosures of which are incorporated byreference).

Conjugation with the vitamin biotin is frequently used since this canreadily be detected by its reaction with enzyme-linked avidin orstreptavidin to which it binds with great specificity and affinity.

However, step (b), (d) and/or step (f) is alternatively performed usingan array. Arrays per se are well known in the art. Typically they areformed of a linear or two-dimensional structure having spaced apart(i.e. discrete) regions (“spots”), each having a finite area, formed onthe surface of a solid support. An array can also be a bead structurewhere each bead can be identified by a molecular code or colour code oridentified in a continuous flow. Analysis can also be performedsequentially where the sample is passed over a series of spots eachadsorbing the class of molecules from the solution. The solid support istypically glass or a polymer, the most commonly used polymers beingcellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride orpolypropylene. The solid supports may be in the form of tubes, beads,discs, silicon chips, microplates, polyvinylidene difluoride (PVDF)membrane, nitrocellulose membrane, nylon membrane, other porousmembrane, non-porous membrane (e.g. plastic, polymer, perspex, silicon,amongst others), a plurality of polymeric pins, or a plurality ofmicrotitre wells, or any other surface suitable for immobilisingproteins, polynucleotides and other suitable molecules and/or conductingan immunoassay. The binding processes are well known in the art andgenerally consist of cross-linking covalently binding or physicallyadsorbing a protein molecule, polynucleotide or the like to the solidsupport. By using well-known techniques, such as contact or non-contactprinting, masking or photolithography, the location of each spot can bedefined. For reviews see Jenkins, R. E., Pennington, S. R. (2001,Proteomics, 2, 13-29) and Lal et al (2002, Drug Discov Today 15; 7(18Suppl):S143-9).

Typically the array is a microarray. By “microarray” we include themeaning of an array of regions having a density of discrete regions ofat least about 100/cm², and preferably at least about 1000/cm². Theregions in a microarray have typical dimensions, e.g., diameters, in therange of between about 10-250 μm, and are separated from other regionsin the array by about the same distance. The array may also be amacroarray or a nanoarray.

Once suitable binding molecules (discussed above) have been identifiedand isolated, the skilled person can manufacture an array using methodswell known in the art of molecular biology.

Hence, the array may be the array is a bead-based array or asurface-based array. Preferably, the array is selected from the groupconsisting of: macroarray, microarray and nanoarray.

In one embodiment, the method according to the first aspect of theinvention comprises:

-   -   (i) labelling biomarkers present in the sample with biotin;    -   (ii) contacting the biotin-labelled proteins with an array        comprising a plurality of scFv immobilised at discrete locations        on its surface, the scFv having specificity for one or more of        the proteins in Table III;    -   (iii) contacting the immobilised scFv with a streptavidin        conjugate comprising a fluorescent dye; and    -   (iv) detecting the presence of the dye at discrete locations on        the array surface

wherein the expression of the dye on the array surface is indicative ofthe expression of a biomarker from Table III in the sample.

In an alternative embodiment step (b), (d) and/or (f) comprisesmeasuring the expression of a nucleic acid molecule encoding the one ormore biomarkers. Preferably the nucleic acid molecule is a cDNA moleculeor an mRNA molecule. Most preferably the nucleic acid molecule is anmRNA molecule.

Hence the expression of the one or more biomarker(s) in step (b), (d)and/or (f) may be performed using a method selected from the groupconsisting of Southern hybridisation, Northern hybridisation, polymerasechain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitativereal-time PCR (q RT-PCR), nanoarray, microarray, macroarray,autoradiography and in situ hybridisation. Preferably, the expression ofthe one or more biomarker(s) in step (b) is determined using a DNAmicroarray.

In one embodiment, the measuring of the expression of the one or morebiomarker(s) in step (b), (d) and/or (f) is performed using one or morebinding moieties, each individually capable of binding selectively to anucleic acid molecule encoding one of the biomarkers identified in TableIII.

In a further embodiment, the one or more binding moieties each compriseor consist of a nucleic acid molecule. Thus, the one or more bindingmoieties may each comprise or consist of DNA, RNA, PNA, LNA, GNA, TNA orPMO. However, it is preferred that the one or more binding moieties eachcomprise or consist of DNA.

Preferably, the one or more binding moieties are 5 to 100 nucleotides inlength. More preferably, the one or more nucleic acid molecules are 15to 35 nucleotides in length. More preferably still, the binding moietycomprises a detectable moiety.

In an additional embodiment, the detectable moiety is selected from thegroup consisting of: a fluorescent moiety; a luminescent moiety; achemiluminescent moiety; a radioactive moiety (for example, aradioactive atom); and an enzymatic moiety. Preferably, the detectablemoiety comprises or consists of a radioactive atom. The radioactive atommay be selected from the group consisting of technetium-99m, iodine-123,iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15,oxygen-17, phosphorus-32, sulphur-35, deuterium, tritium, rhenium-186,rhenium-188 and yttrium-90.

However, the detectable moiety of the binding moiety may be afluorescent moiety (for example an Alexa Fluor dye, e.g. Alexa647).

In one embodiment the sample provided in step (b), (d) and/or (f) isselected from the group consisting of unfractionated blood, plasma,serum, tissue fluid, pancreatic tissue, pancreatic juice, bile andurine. Preferably, the sample provided in step (b), (d) and/or (f) isselected from the group consisting of unfractionated blood, plasma andserum. More preferably, the sample provided in step (b), (d) and/or (f)is plasma. In another preferred embodiment, the sample provided in step(b), (d) and/or (f) is serum.

A second aspect of the present invention provides an array fordetermining the presence of pancreatic cancer in an individualcomprising one or more binding agent as defined in the first aspect ofthe present invention.

Arrays suitable for use in the methods of the invention are discussedabove.

Preferably the one or more binding agents are capable of binding to allof the proteins defined in Table III.

A third aspect of the present invention provides the use of one or morebiomarkers selected from the group defined in the first aspect of theinvention as a diagnostic marker for determining the presence ofpancreatic cancer in an individual. Preferably, all of the proteinsdefined in Table III are used as diagnostic markers for determining thepresence of pancreatic cancer in an individual.

A fourth aspect of the present invention provides a kit for determiningthe presence of pancreatic cancer comprising:

-   -   A) one or more first binding agent according to the first aspect        of the invention or an array according the second aspect of the        invention; and    -   B) instructions for performing the method according to the first        aspect of the invention.

Preferred, non-limiting examples which embody certain aspects of theinvention will now be described, with reference to the following tablesand figures:

FIGS. 1A-1F: Classification of PaC vs. N

FIG. 1A) Scanned image of an antibody microarray hybridized with a PaCserum. In total, 160 probes, including position markers and controls,were printed in eight 20×8 subarrays per slide. FIG. 1B) Differentiallyexpressed (p<0.05) serum analytes for PaC vs. N. FIG. 1C) ROC curve forPaC vs. N based on all antibodies, i.e. using unfiltered data. FIG. 1D)Classification of PaC vs. N, using the SVM prediction values based onall antibodies (red dots-PaC, blue dots-N). The relative expressionlevels of the top 20 differentially expressed (p<0.02) non-redundantanalytes are shown in a heatmap. Red—up-regulated, green—down-regulated,black—equal levels. (FIG. 1E) Validation of scFv antibody specificity,illustrated for a highly differentially expressed (p=0.005) analyte,IL-6, using a 278 human protein array. (FIG. 1F) Validation of scFvantibody specificity, illustrated for a modestly differentiallyexpressed (p=0.04) analyte, IL-10, using a 278 human protein array.

FIGS. 2A-2D: Pre-validation of biomarker signature for PaC vs. Nclassification

(FIG. 2A) Condensation of the biomarker signature for PaC vs. Nclassification in the first patient cohort using a LOO procedurecombined with a backward elimination strategy. The observed ROC AUCvalues were plotted against the remaining number of antibodies. (FIG.2B) The condensed 18-analyte non-redundant biomarker signature obtainedfrom the first patient cohort. (FIG. 2C) The first patient cohort wasused as training set, and the output classifier was then tested on anew, independent patient group, the second patient cohort. (FIG. 2D)Pre-validation of the biomarker signature for PaC vs. N classificationillustrated by the ROC curve obtained for the classifier on the testset.

FIGS. 3A-3C: Candidate serum biomarker signatures differentiating PaCand pancreatitis

(FIG. 3A) Differentially expressed (p<0.05) serum analytes for PaC vs.ChP, AIP or ChP+AIP+N, respectively. (FIG. 3B) ROC curves for PaC vsChP, AIP, or ChP+AIP+N classification based on all antibodies, i.e.using unfiltered data. (FIG. 3C) Validation of the antibody microarraydata of selected analytes using a 10-plex cytokine sandwich antibodymicroarray (MSD). Data is only shown for the only analyte, IL-8, forwhich the majority of the observed signals were above the lower limit ofdetection for the MSD assay.

FIGS. 4A-4E: Pre-validation of a candidate serum biomarker signature forPaC diagnosis

(FIG. 4A) The first patient cohort, composed of PaC, N, ChP and AIP, wassplit into a training set (two thirds) and a test set (one third). (FIG.4B) The condensed 25 non-redundant serum biomarker signature obtainedfor the training set using a backward elimination strategy. (FIG. 4C)Pre-validation of the condensed 25-analyte biomarker signature for PaCdiagnosis, as illustrated by the ROC curve obtained for the classifieron the test set. (FIG. 4D) Performance, expressed as ROC AUC values, ofthe condensed biomarker signature obtained by the backward eliminationstrategy (solid line) as compared to that of 25-analyte signaturesobtained by either i) 1000 random 25-marker signatures (open circles),ii) lowest p-values (dashed line), or iii) highest fold-change (dottedline). (FIG. 4E) Comparison of the ROC AUC value obtained for thecondensed 25-analyte biomarker signature on the test set, when thesample annotation was correct (solid line) or permutated a 1000 times(open circles).

FIG. 5: Schematic outline of the antibody microarray strategy

FIG. 6: ROC-AUC values for differentiation between pancreatic cancer,and normal, chronic pancreatitis, and/or acute inflammatory pancreatitis

ROC-AUC values are shown for marker signatures having all of the TableIV(A) (i.e., core) and Table(B) (i.e., preferred) markers, andincreasing numbers of Table(C) (i.e., optional) markers. The best ROCAUC value (0.90) is obtained for a 29 analyte signature, i.e., coremarkers+preferred markers+15 optional markers. However, all markercombinations had substantial predictive power.

EXAMPLES

Materials and Methods

Serum Samples

Serum samples were collected at the time of diagnosis, i.e. prior tocommencing any therapy, from two independent patient cohorts and storedat −80° C. In the first cohort, serum samples from 103 patients,diagnosed with pancreatic cancer (PaC) (n=34), chronic pancreatitis(ChP) (n=16), autoimmune pancreatitis (AIP) (n=23), or healthyindividuals (N) (n=30) (no clinical symptoms) were screened. The patientdemographics are described in Table 1. This cohort was randomly splitand used as training set and test set. The second cohort, comprised of45 patients, diagnosed with PaC (n=25), or N (n=20) (for patientdemographics, see [14]), was used as an independent test set only, usingantibody microarray data recently collected [14]. The size of the samplecohorts was limited by the availability of well-characterized serumsamples collected at the time of diagnosis.

Antibody Microarray Analysis

The recombinant antibody microarray analysis was performed usingpreviously in-house optimized protocols [12-15] (see below). Briefly,121 human recombinant single-chain Fv (scFv) antibodies, targeting 57mainly immunoregulatory analytes, were used as probes. The specificity,affinity (nM range), and on-chip functionality of the phage-displayderived scFvs [16] was ensured by using i) stringent selection protocols[16], ii) multiple clones (4) per target analyte, and iii) a scFvlibrary microarray adapted by molecular design (REF). The planarantibody microarrays (array size; 160×8, <0.5 cm²) were prepared bydispensing the antibodies and controls one-by-one (330 μL/drop) using anon-contact dispenser. The biotinylated serum samples were separatelyscreened and specifically bound analytes were visualized by addingfluorescently labelled streptavidin using a confocal fluorescencescanner. Each individual array data point represents the mean value offour replicates. Chip-to-chip normalization was performed by using asemi-global normalization approach. In accordance to previous studies[12, 15, 17], the correlation coefficient for spot-to-spotreproducibility and array-to-array reproducibility was 0.99 and 0.94,respectively. Selected antibody specificities and microarray data werevalidated (Table II) using a 234 human protein array and a 10-plexcytokine sandwich antibody microarray, respectively. In addition,several antibody specificities have previously been validated usingELISA, protein arrays, blocking/spiking experiments, and/or massspectrometry (Table II).

Microarray Data Analysis

The data analysis was performed in R (see below). Briefly, a supportvector machine (SVM) was employed to classify the samples as belongingto one of two defined groups (e.g. cancer vs. healthy), using a linearkernel with the cost of constraints set to 1. No attempts were made totune it in order to avoid the risk of over-fitting. The SVM was trainedand tested using a leave-one-out (LOO) cross validation procedure. Intwo of the comparisons, this training part included the creation of anantibody sub-panel by selecting antibodies that, in the training set,displayed the highest discriminatory power. This selection of antibodieswas made using either a direct or a cross-validated backward eliminationstrategy. Using this approach, condensed candidate biomarker signatureswere identified, and subsequently evaluated on independent test sets.

Sensitivity and specificity values were calculated from the SVM decisionvalues, using a threshold level of zero. A receiver operatingcharacteristics (ROC) curve was constructed using the SVM decisionvalues. The area under the curve (AUC) was calculated and used as ameasure of prediction performance. Further, the Wilcoxon p-value and thefold change were calculated for each antibody. The candidate biomarkersignatures were reported following the recommendations for tumour markerprognostic studies [18].

Serum Samples

After informed consent, serum samples were collected from twoindependent patient cohorts at the time of diagnosis, i.e. prior toinitiation of therapy, and stored at −80° C. PC was verified withhistology. Patient cohort 1 was composed of serum samples from 103patients (Mannheim University Hospital, Germany), diagnosed withpancreatic ductal adenocarcinoma (PaC) (n=34), chronic pancreatitis(hCP) (n=16), autoimmune pancreatitis (AIP) (n=23), or healthyindividuals (controls; N) (n=30) (no clinical symptoms). The patientdemographics are described in Table 1. This cohort was also randomlysplit and used as training set (two thirds of the samples) and test set(one third). Patient cohort 2 was composed of 45 patients, diagnosedwith PaC (n=25), or N (n=20) (Stockholm South General Hospital and LundUniversity Hospital, Sweden) (for patient demographics, see [39]), andwas adopted using antibody microarray data as recently described [39]. Apower analysis (see below) was performed in order to confirm that thesize of the sample cohorts was sufficient to provide a statisticalpower >80%. The main experiments were performed on patient cohort 1,while cohort 2 was used as an independent data set for validation in oneexperiment (see FIG. 5).

Labelling of Serum Samples

The serum samples were labelled using previously optimized labellingprotocols for serum proteomes [39-43]. Briefly, crude serum samples werethawed on ice and 30 μL aliquots were centrifuged (16 000×g for 20 minat 4° C.). Five μL of the supernatant was diluted 45 times in PBS,resulting in a protein concentration of approximately 2 mg/mL. Sampleswere labelled with EZ-Link® Sulfo-NHS-LC-Biotin (Pierce, Rockford, Ill.,USA) at a final concentration of 0.6 mM for 2h on ice with gentlyvortexing every 20 min. Free biotin was removed by dialysis against PBSfor 72 h at 4° C. using 3.5 kDa MW cut-off dialysis units (ThermoScientific, Rockford, Ill., USA). The samples were aliquoted and storedat −20° C.

Production and Purification of scFv

In total, 121 human recombinant single-chain Fv (scFv) antibodyfragments, targeting 57 mainly immunoregulatory biomolecules wereselected from the n-CoDeR library [43] and kindly provided by BiolnventInternational AB, Lund, Sweden, or provided by Prof. Mats Ohlin (LundUniversity, Sweden) (5 clones against mucin-1). The specificity,affinity (nM range) and on-chip functionality of the phage-displayderived scFv was ensured by using i) stringent selection protocols [43],ii) multiple clones (4) per target molecule, and iii) a scFv librarymicroarray adapted by molecular design [44, 45]. The antibody fragmentswere produced in 100 mL E. coli cultures and purified from eitherexpression supernatants or cell periplasm, using affinity chromatographyon Ni-NTA agarose (Qiagen, Hilden, Germany). Bound molecules were elutedwith 250 mM imidazole, extensively dialysed against PBS and stored at 4°C. until used for microarray fabrication. The antibody concentration wasdetermined by measuring the absorbance at 280 nm (average 500 μg/mL,range 50-1840 μg/mL).

Fabrication and Processing of Antibody Microarrays

For the production of planar antibody microarrays, we used a set-uppreviously optimized and validated [39-43, 46]. Briefly, scFvs werearrayed onto black polymer Maxisorb microarray slides (NUNC, Roskilde,Denmark) using a non-contact printer (BioChip Arrayer, PerkinElmer Life& Analytical Sciences, Wellesley, Mass., USA) by depositingapproximately 330 μL drops, using piezo technology. Two drops werespotted in each position, allowing the first drop to dry out before thesecond drop was dispensed. In average, 5 fmol antibody (rang 1.5-25) wasdeposited per position. In order to ensure adequate statistics and toaccount for any local defects, each probe was printed in eightreplicates. In total, 160 probes, including position markers and controlscFvs were printed per slide, oriented in eight 20×8 subarrays. Toassist grid alignment during quantification, a row of Alexa647conjugated Streptavidin (Invitrogen, Carlsband, CA, USA) (10 μg/mL) wasspotted at selected positions. The arrays were blocked in 5% (w/v)fat-free milk powder (Semper A B, Sundbyberg, Sweden) in PBS over night.

The microarray slides were processed in a ProteinArray Workstation(PerkinElmer Life & Analytical Sciences) according to a previouslydescribed protocol [42]. Briefly, the arrays were washed with 0.5% (v/v)Tween-20 in PBS (PBS-T) for 4 min at 60 μL/min and then incubated with75 μL biotinylated serum sample (diluted 1:2, resulting in a total serumdilution of 1:90) in 1% (w/v) fat-free milk powder and 1% (v/v) Tween-20in PBS (PBS-MT), for 1h with agitation every 15th second. Next, thearrays were again washed with PBS-T and incubated with 1 μg/mL Alexa-647conjugated streptavidin in PBS-MT, for 1 h. Finally, the arrays werewashed with PBS-T, dried under a stream of nitrogen gas and scanned witha confocal microarray scanner (PerkinElmer Life & Analytical Sciences)at 10 μm resolution, using four different scanner settings of PMT gainand laser power. The intensity of each spot was quantified in theScanArray Express software v.4.0 (PerkinElmer Life & AnalyticalSciences), using the fixed circle method. The local background wassubtracted. To compensate for any possible local defects, the twohighest and the two lowest replicates were automatically excluded andthe mean value of the remaining four replicates was used. For antibodiesdisplaying saturated signals, values from lower scanner settings werescaled and used instead. Chip-to-chip normalization was performed usinga semi-global normalization approach previously described [39, 40, 42].First, the CV for each probe over all samples was calculated and ranked.Second, 15% of the probes that displayed the lowest CV-values over allsamples were identified and used to calculate a chip-to-chipnormalisation factor for each array. The normalization factor N, wascalculated by the formula N, ═S_(i)/μ, where S_(i) is the sum of thesignal intensities for the antibodies used, averaged over all samplesand p is the sample average of S_(i). The intensities were recalculatedto log 2 values prior to statistical analysis.

Validation of Antibody Specificity

The specificities of two selected scFvs (anti-IL-6 (2) and anti-IL-10(1)) were tested using RayBio® 278 Human Protein Array G series(Norcross, Ga., USA), according to protocol provided by themanufacturer. The scFvs were labelled with EZ-Link® Sulfo-NHS-LC-Biotin(Pierce) for 2h on ice at a 3.5 times molar excess of biotin. Unboundbiotin was removed by 72 h dialysis against PBS. In total, 5 μg ofantibody was added to each array. Binding was detected using 1 μg/mLAlexa647 conjugated Streptavidin (Invitrogen). PBS was added to onearray as a negative control to check for unspecific binding ofStreptavidin. The arrays were scanned and the signals from the negativecontrol array were subtracted. In addition, several antibodyspecificities have previously been validated using well-characterized,standardized serum samples, and independent methods, such as massspectrometry, ELISA, MSD, and CBA, as well as using spiking and blockingexperiments (Table II).

Validation of array data A human Th1/Th2 10-plex MSD (Meso ScaleDiscovery, Gaithersburg, Md., USA) assay was run in an attempt tovalidate the antibody microarray results. Each well of the MSD 96-platehad been pre-functionalized with antibodies against IFN-γ, IL-1β, IL-2,IL-4, IL-5, IL-8, IL-10, IL-12p70, IL-13 and TNF-α in spatially distinctelectrode spots. A total of 34 serum samples (undiluted) were analyzed,including 11 PaC, 11 healthy, 9 ChP and 3 AIP samples (the low number ofAIP samples was due to limited sample volumes in that subgroup). Theassay was run according to the protocol provided by the manufacturer andthe electrochemiluminiscence-based readout was performed in an MSDSECTOR® instrument.

Microarray Data Analysis

All statistics and data analysis was performed in R(http://www.r-project.org). Briefly, a support vector machine (SVM) wasemployed to classify the samples as belonging to one of two definedgroups (e.g. cancer or healthy), using a linear kernel with the cost ofconstraints set to 1. No attempts were made to tune it in order to avoidthe risk of over-fitting. The SVM was trained and tested using aleave-one-out (LOO) cross validation procedure [42]. In two of thecomparisons, this training part included creating an antibody sub-panelby selecting antibodies that, in the training set, displayed the highestdiscriminatory power. This selection of antibodies was made using eithera direct or a cross-validated backward elimination strategy. Using thisapproach, condensed candidate biomarker signatures were identified, andsubsequently evaluated on independent test sets.

Sensitivity and specificity values were calculated from the SVM decisionvalues, using a threshold level of zero. A receiver operatingcharacteristics (ROC) curve was constructed using the SVM decisionvalues. The area under the curve (AUC) was calculated and used as ameasure of prediction performance. Further, the Wilcoxon p-value and thefold change were calculated for each antibody. The candidate biomarkersignatures were reported following the recommendations for tumour markerprognostic studies [47].

Biomarker Signatures Identification

A backward elimination procedure was used for identifying a biomarkersignature for distinguishing PaC from healthy individuals. In thisapproach, one sample at the time was excluded from the dataset. Theremaining samples were used for training the SVM by excluding oneantibody at the time and performing the classification using theremaining antibodies. When all antibodies had been left out once, theleast informative antibody was defined as the one that had been excludedwhen the smallest Kullback-Leibler (KL) error was obtained for theclassification, and was eliminated from the dataset. The LOO procedurewas iterated until only one antibody was left and the order by which theantibodies had been eliminated was recorded. The procedure was repeatedby excluding a new sample and the iteration continued until all sampleshad been left out once. A list of the order in which the antibodies wereeliminated was generated for each time a sample was excluded. In theend, all samples had been left out once and a consensus list was createdwhere each antibody was assigned a score based on how long it hadendured the elimination process, averaged over all iterations performed.Throughout the process, each left out sample was used to test the SVMmodels built for each new length of antibody subpanels, returning adecision value corresponding to the performance. Consequently, decisionvalues for all samples for any given subpanel length were collected. Thecorresponding ROC areas were plotted against number of antibodies as ameans to evaluate the strength of the data set and the eliminationstrategy. A condensed signature of 18 analytes was selected from theconsensus list and an independent data set from antibody microarrayanalysis of 25 PaC and 20 N serum samples [39] was used as a test setfor pre-validation of the candidate signature. The signature analyteswere used in a SVM LOO cross validation procedure in the test set andthe ability of the signature to distinguish PaC from N was illustratedin a ROC curve.

A second candidate biomarker signature was generated for classificationof PaC among both N, ChP and AIP. First, the data was randomly dividedinto a training set (two thirds of the samples) and a test set (onethird). A modified (even more stringent) backward elimination strategywas used. Instead of leaving out one sample at the time, the SVM wastrained only once, using all samples in the training set. Consequently,one elimination list was generated from which a condensed panel of 25analytes was selected and used to build the SVM in the training set. Themodel was applied onto the independent test set and a ROC curve wasgenerated. Furthermore, a statistical power analysis was performed toestimate the number of patients required in the test set using thefunction “power.t.test” in R (decision values assumed normallydistributed as suggested by Shapiro-Wilk testing). The observed decisionvalues from the SVM analysis in the training set displayed a standarddeviation of 2.87 and a delta value between the groups of 3.47(difference between mean values). The alpha level (level ofsignificance) was set to 0.05. In addition, the validity of thisbackward elimination procedure was tested by comparing the performanceof the selected signature to 1000 randomly generated signatures of thesame length and to signatures generated by selecting the antibodies ofthe lowest p-value and highest fold-change, respectively. Finally, thestrength of the classifier and the data set was tested by comparing theperformance of the signature in the test dataset to random data, bygenerating 1000 permutation of the sample annotations in the test dataset.

Results

Classification of PaC Vs. Healthy Controls

In order to identify serum biomarker signature associated with PaC, weperformed differential serum protein expression profiling of PaC (n=34)vs. N (n=30), using the first patient cohort. A representative image ofan antibody microarray is shown in FIG. 1A, illustrating that dynamicsignal intensities, adequate spot morphology and low non-specificbackground binding were obtained. The results showed that 33non-redundant protein analytes, including e.g. both Th1 and Th2cytokines, were found to be differentially expressed (p<0.05), of whichall, but the complement proteins C1q and Properdin, were up-regulated inPaC (FIG. 1B).

To investigate whether PaC and N could be differentiated, we ran a SVMLOO cross-validation, based on all antibodies, i.e. using unfiltereddata. The data showed that the patient cohorts could be classified witha ROC AUC value of 0.94 (FIG. 1C). In FIG. 1D, the samples are plottedby decreasing SVM decision value, and the relative expression pattern ofthe top 20 differentially expressed analytes (p<0.02) are shown in aheat map. By using a threshold of 0 (default value), the analysis showedthat PaC vs. N could be classified with a sensitivity and specificity of82% and 87%, respectively.

Next, a 278 human protein array was used for validation of selectedantibody specificities (FIGS. 1E and 1F). To this end, scFv antibodiesagainst one highly differentially expressed analyte, IL-6 (p=0.005), andone modest differentially expressed analyte, IL-10 (p=0.04), wereselected. In both cases, the protein array analysis showed that the scFvantibody fragments bound specifically to their target protein.

Pre-Validation of Condensed Biomarker Signature for PaC Vs. NClassification

In order to test the strength of the classification derived from thefirst patient cohort (n=64), we first condensed the total number ofanalytes down to the 18 non-redundant biomarkers contributing the mostto the classification, by combining our LOO procedure with an iterativebackward elimination strategy. In this process, the Kullback-Leiblerdivergences error was minimized and used as guide for stepwise removalof the antibodies one-by-one. After each round, the SVM decision valueswere collected and the corresponding ROC curve and AUC value werecalculated. In FIG. 2A, the AUC value is plotted against the number ofremaining antibodies, indicating a high and stable classification evenwhen only a few antibodies were included. The 18-analyte condensedcandidate serum biomarker signature, composed of a variety of analytes,e.g. cytokines, complement proteins and enzymes, is shown in FIG. 2B.Next, we applied this 18-analyte classifier on a new independent testgroup, the second patient cohort (n=45) (FIG. 2C). The results showedthat the classifier allowed a stratification of patients into PaC vs. Nwith a ROC AUC value of 0.95 (FIG. 2D), corresponding to a sensitivityof 88% and specificity of 85%. Hence, the data outlined the firstpre-validated serum biomarker signature for PaC diagnosis.

Biomarker Signatures Differentiating PaC Vs. Pancreatitis

To test whether cancer could be differentiated from benign conditions inthe pancreas, we compared the serum protein expression profile of PaC(n=34) with that of ChP (n=16) or AIP (n=23) using the first patientcohort. In the case of PaC vs. ChP, 15 non-redundant differentiallyexpressed (p<0.05) serum analytes were pin-pointed, of which all but two(IL-4 and IL-12) were up-regulated in PaC (FIG. 3A). Based on unfiltereddata, the results showed that PaC and ChP could be differentiated with aROC AUC value of 0.86 (FIG. 3B), corresponding to a 97% sensitivity and69% specificity. A total of 49 non-redundant serum analytes were foundto be differentially expressed in PaC vs. AIP, with all except for C1qand Properdin, being up-regulated in PaC (FIG. 3A). Again, based onunfiltered data, the results showed that PaC vs. AIP could be classifiedwith a ROC value of 0.99 (FIG. 3B), based on a sensitivity andspecificity of 97% and 91%, respectively.

To better reflect the clinical reality, we then investigated whetherdifferences could be deciphered between PaC and the combined,heterogeneous patient group of ChP+AIP+N, using the first patient cohort(n=103). The results showed that 47 non-redundant serum proteins weredifferentially expressed (p<0.05) (FIG. 3A). A majority of the analytes(45 of 47) were found to be up-regulated in PaC, including a wide rangeof proteins. Based on unfiltered data, the results showed that PaC couldbe distinguished from this heterogeneous patient group with a ROC AUCvalue of 0.85 (FIG. 3B).

In an attempt to validate the array data, an independent 10-plexcytokine sandwich antibody microarray (MSD) was applied (FIG. 3C).However, only 1 of 10 targeted serum analytes, IL-8, was above the lowerlimit of detection of the MSD assay in a majority of the samples. Still,the observed up-regulation of IL-8 in PaC vs. N, ChP, AIP and combinedcohort thereof was statistically confirmed (p<0.05) by the MSD assay inall cases, except for PaC vs. AIP (p=0.29).

Refined Biomarker Signature for PaC Diagnosis

To test the strength of the classification of the entire first patientcohort, including PaC, N, ChP and AIP (n=103), we split the cohort intoa training set (two thirds) and test set (one third) (FIG. 4A). Next, acondensed serum biomarker signature composed of the 25 non-redundantanalytes contributing the most to the classification in the training setwas deciphered by using a direct, iterative backward eliminationstrategy. The 25-analyte condensed biomarker signature, composed of e.g.cytokines and complement proteins, is shown in FIG. 4B. Next, we appliedthis 25-analyte classifier on the independent test set (FIG. 4C). Thedata showed that PaC could be pinpointed with a ROC AUC value of 0.88(FIG. 4C), outlining a sensitivity and specificity of 73% and 75%,respectively.

To further challenge the classifier, we statistically evaluated itsdiscriminatory power. Firstly, 1000 random signatures of the same length(25 antibodies) were generated in the training set and applied to thetest set. The results showed that the AUC values for the randomsignatures were lower than that of the classifier biomarker signature(AUC=0.88) in 95% of the cases (FIG. 4D). In addition, the AUC valuesfor the corresponding 25-analyte signature selected based on eitherlowest p-values (AUC=0.77) or highest fold-changes (AUC=0.78) weresignificantly lower than that of the classifier signature. Hence, thedata further indicated the discriminatory power of the classifier, andthe applicability of the backward elimination strategy for defining acondensed, high-performing signature. Secondly, the sample annotation ofthe test set was permutated 1000 times in order to compare the specificclassification to random classification of the same number of samples.The results showed that a significantly higher AUC value (0.88 vs.0.19-0.86, median value of 0.5) was obtained when the correct sampleannotation was used than when the random annotation was applied, furtherdemonstrating the strength of the classification.

Discussion

In this study, we have applied affinity proteomics in order to harnessthe diagnostic power of the immune system to target PaC. We based ourapproach on the notion that the immune system is exquisitely sensitiveto alterations in an individual's state of health, resulting fromdisease, registering these changes through fluctuations in the levelsof, in particular, immunoregulatory analytes. To this end, we designedour antibody microarray to target predominantly these kinds of keyregulatory serum analytes. The data showed that PaC-associated candidatebiomarkers signatures displaying high diagnostic power could bede-convoluted. In a similar fashion, this affinity proteomic approachhas recently allowed the identification of several serological biomarkersignatures distinguishing other cancer indications and healthy controls[14-15, 17, 19], further demonstrating the strength of the platform.

We showed for the first time that serum stored information enabling usto discriminate between not only well-defined patient cohorts of PaC vs.controls and PaC vs. pancreatitis, but also between PaC vs. the combinedcohort of controls and pancreatitis patients with high confidence. Thislatter finding was in particular critical, since the candidate biomarkersignatures must perform well also in clinical settings whereheterogeneous patients groups will be screened.

The clinical impact of a high-performing PaC classifier would be high asno validated serological discriminator is yet in place [2, 7-9, 20-21].While waiting for a golden classifier to be established, CA-19-9 remainsthe most useful molecular marker for PaC diagnosis [2, 8-10]. Notably,our data showed a significantly higher median sensitivity (88%) andspecificity (85%) for PaC diagnosis than what have been consistentlyobserved for CA-19-9 [2, 9-11], outlining a significant clinically addedvalue. Further, we have recently modelled the impact of new diagnosticpossibilities on cost, survival, and quality of life for risk patients,and showed that affinity proteomics had great prospects for becoming acost-effective tool in screening for PaC (Bolin et al, ms in prep.).

The classifier will perform at its best if early diagnosis, when thetumour is still small and operable, could be performed [2, 9].

In the quest for cancer biomarkers, systemic inflammation is frequentlyhighlighted as a potential confounding factor [23], since cancerdevelopment and inflammation has been linked. In early works based onaffinity proteomics, the results also often showed that general disease(inflammatory) signatures rather than cancer-specific fingerprints weredelineated [24-26]. Notably, we showed here that PaC and pancreatitiscould be discriminated with high confidence. Furthermore, the observedsignature(s) showed significant differences, i.e. only small overlaps,with those observed for other various inflammatory conditions refs [19,27] (Carlsson et al., ms in prep.) and other cancers [14-15, 17, 19],further supporting the notion that PaC-specific signatures weredeciphered.

The serum immunosignatures could be considered as snapshots of theimmune system's activity in a patient at the time of the test. Thesefingerprints will reflect a combination of direct and indirect(systemic) effects in response to the cancer. Focusing on the cytokineexpression profiles, previous reports have shown that pancreatic cancercell lines expressed a set of cytokines found to be over expressed alsoin this study, including e.g. IL-6, IL-8, IL-10, IL-12, IL-13, IL-18,and TGF-β1 [28]. Several of these and other cytokines (e.g. VEGF andIL-7) have also been found to be overexpressed in PaC tumour tissueand/or serum/plasma [29-33] further supporting our observations.Although cytokines play a pivotal role in the immune system,interpreting these intricate expression patterns in a biological contextis demanding since many of these analytes display pleiotropic functionsand PaC is characterized by peculiar cytokine expression patterns [29].While the expression of e.g. IFN-γ could signal an attempted anti-tumourimmune response [29], the immunological environment of PaC has oftenbeen found to be in an immunosuppressive site, as illustrated by theconcomitant expression of anti-inflammatory cytokines (e.g. TGF-β andIL-10), and potentially inactive proinflammatory cytokines (e.g. IL-12and IL-18) [29]. A cellular immunosuppression is a striking biologicalfeature of PaC observed in many patients [34]. While Th2 skewedresponses have been reported, the Th1/Th2 balance indicated here hasalso been observed [29, 31, 35]. The cytokine expression pattern hasalso been found to reflect other parameters, such as survival [14, 29].Looking at some of the non-cytokine markers, several complementsproteins, such as C3, which has been suggested to function in immunesurveillance against tumours [36-37], and the carbohydrate antigen Lewisx have also previously been found to be associated with PaC [38].

Taken together, we have addressed a clinical need and demonstrated thatimmunosignaturing was a powerful approach for deciphering the firstpre-validated serological biomarker signatures for PaC diagnosis. Thiswas achieved through a high-performing platform, well-controlled samplesand stringent bioinformatic and validation approaches. The potential ofthe predictor signature will be further validated in follow-up studies,in which independent sample cohorts will be profiled. In the end, thesefindings will provide novel opportunities for improved PaC diagnosis andthereby enhanced prognosis and clinical management of PaC.

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TABLE I Patient demographics of the first patient cohort Age No. ofGender Mean Class patients (M/F/unknown) (SD) Range PaC 34 18/12/4 65.042-93 (10.4) N 30 15/15/0 33.2 24-53 (8.6) ChP 16 12/4/0 48.8 32-73(14.2) AlP 23 11/11/1 42.4 14-74 (18.3) All 103 56/42/5 48.2 14-93(18.1)

TABLE II Summary of serum biomarkers analyzed by the antibodymicroarrays Antigen (no. of clones) Angiomotin (2) β-galactosidase (1)Bruton tyrosine kinase BTK (1) C1 Esterase inhibitor (1) C1q (1)** C1s(1) C3 (2)** C4 (1)** C5 (2)** CD40 (4) CD40 ligand (1) CT-17 (control)(1) Digoxin (control) (1) Eotaxin (3) Factor B (1)** GLP-1 (1) GLP-1 R(1) GM-CSF (3) IFN-γ (2) IgM (1) IL-10 (3)* IL-11 (3) IL-12 (4)** IL-13(2)** IL-16 (2) IL-18 (3) IL-1α (3)** IL-1β (3) IL-1-ra (3) IL-2 (3)IL-3 (3) IL-4 (4)** IL-5 (3)** IL-6 (4)*/** IL-7 (2) IL-8 (3)** IL-9 (3)Integrin α10 (1) Integrin α11 (1) Leptin (1) Lewis x (2) Lewis y (1)MCP-1 (3)** MCP-3 (1) MCP-4 (2) Mucin-1 (6) Procathepsin W (1) Properdin(1)** PSA (1) RANTES (2) Sialyl Lewis x (1) TGF-β1 (3) TM peptide (1)TNF-α (2) TNF-β (4)** Tyrosine protein kinase JAK3 (1) VEGF (4)***Antibody specificity determined by protein array. **Antibodyspecificity previously validated by ELISA, protein array,blocking/spiking experiments, and/or mass spectrometry.

TABLE III Pancreatic Cancer Diagnostic Biomarkers Biomarker nameExemplary sequences Interleukin-7 (IL-7) AK226000, AB102893, AB102885,P13232 Integrin α-10 Hs158237; 075578 B-galactosidase P16278 Bruton'styrosine kinase (BTK) Q06187 Complement protein C1q (C1q) IPR001073,PR00007 Complement protein C1s (C1s) P09871 B cell receptor μ□chain(IgM) e.g. P01871 (not complete protein); isotype-specific for IgM onRamos B cells¹⁾ Interleukin-9 (IL-9) P15248 Integrin α-11 Q9UKX5 Januskinase 3 protein tyrosine P52333 kinase (JAK3) Procathepsin W P56202Properdin P27918 TM peptide (10TM protein) NA - see above Tumournecrosis factor-α (TNF-α) P01375 Angiomotin AAG01851; Q4VCS5Complement-1 esterase inhibitor P05155 (C1-INH) Complement protein C3(C3) BC150179, BC150200; P01024 Complement protein C4 (C4) BC151204,BC146673, AY379959, AL645922, AY379927, AY379926, AY379925 Complementprotein C5 (C5) BC113738, BC113740, DQ400449, AB209031, P01031 CD40Q6P2H9 Eotaxin P51671 Complement Factor B (Factor B) P00751Glucagon-like peptide-1 (GLP-1) Glucagon-like peptide-1 receptor P43220(GLP-1 R) Granulocyte-macrophage colony- P04141 stimulating factor(GM-CSF) Interleukin-10 (IL-10) P22301 Interleukin-11 (IL-11) P20809Interleukin-12 (IL-12) O60595 Interleukin-13 (IL-13) P35225Interleukin-18 (IL-18) Q14116 Interleukin-1α (IL-1α) P01583Interleukin-1β (IL-1β) P01584 Interleukin-2 (IL-2) P60568 Interleukin-3(IL-3) P08700 Interleukin-4 (IL-4) P05112 Interleukin-5 (IL-5) BC066282,CH471062, P05113 Interleukin-6 (IL-6) P05231 Interleukin-8 (IL-8)CR623827, CR623683, DQ893727, DQ890564, P10145 Interferon-γ (INF-γ)P01579 Leptin P41159 Lewis X/CD15 Carbohydrate structure (notapplicable) Lewis y Carbohydrate structure (not applicable) Monocytechemotactic protein-1 P13500 (MCP-1) Mucin-1 P15941 Prostate specificantigen (PSA) P07288 RANTES P13501 Sialyl Lewis x Carbohydrate structure(not applicable) Transforming growth factor-1 P01137 (TGF-b1) Tumournecrosis factor-β (TNF-β) P01374 Vascular endothelial growth factorP15692, P49765, P49767, =O43915 (VEGF) CD40 ligand P29965 Interleukin-16(IL-16) Q05BE6, Q8IUU6, B5TY35 Interleukin-1ra (IL-1ra) P18510 Monocytechemotactic protein-3 BC112258, BC112260, BC092436, BC070240 (MCP-3)Monocyte chemotactic protein-4 Q99616 (MCP-4)

TABLE IV Pancreatic Cancer Diagnostic Biomarkers Biomarker name (A) Corebiomarkers Interleukin-7 (IL-7) Integrin α-10 (B) Preferred biomarkersB-galactosidase Bruton's tyrosine kinase (BTK) Complement protein C1q(C1q) Complement protein C1s (C1s) B cell receptor μ□chain (IgM)Interleukin-9 (IL-9) Integrin α-11 Janus kinase 3 protein tyrosinekinase (JAK3) Procathepsin W Properdin TM peptide Tumour necrosisfactor-α (TNF-α) (C) Optional additional biomarkers AngiomotinComplement-1 esteras inhibitor (C1-INH) Complement protein C3 (C3)Complement protein C4 (C4) Complement protein C5 (C5) CD40 EotaxinComplement Factor B (Factor B) Glucagon-like peptide-1 (GLP-1)Glucagon-like peptide-1 receptor (GLP-1 R) Granulocyte-macrophagecolony-stimulating factor (GM-CSF) Interleukin-10 (IL-10) Interleukin-11(IL-11) Interleukin-12 (IL-12) Interleukin-13 (IL-13) Interleukin-18(IL-18) Interleukin-1α (IL-1α) Interleukin-1β (IL-1β) Interleukin-2(IL-2) Interleukin-3 (IL-3) Interleukin-4 (IL-4) Interleukin-5 (IL-5)Interleukin-6 (IL-6) Interleukin-8 (IL-8) Interferon-γ (IFN-γ) LeptinLewis X/CD15 Lewis y Monocyte chemotactic protein-1 (MCP-1) Mucin-1Prostate specific antigen (PSA) Rantes Sialyl Lewis x Transformigngrowth factor-1 (TGF-b1) Tumour necrosis factor-β (TNF-β) Vascularendothelial growth factor (VEGF) CD40 ligand Interleukin-16 (IL-16)Interleukin-1ra (IL-1ra) Monocyte chemotactic protein-3 (MCP-3) Monocytechemotactic protein-4 (MCP-4)

TABLE V Pancreatic Cancer Diagnostic Biomarker Subsets PaC vs N PaC vsN + Chp + AlP PaC vs ChP PaC vs AlP Biomarker name P value Backward Pvalue backward P value P value A CD40 X X X X X Interleukin-12 (IL-12) XX X X X X Interleukin-3 (IL-3) X X X X X X Interleukin-4 (IL-4) X X X XX Interleukin-8 (IL-8) X X X X Monocyte chemotactic protein-1 (MCP-1) XX X X X Mucin-1 X X X X X Transforming growth factor, beta-1 (TGF-b1) XX X X X X Tumour necrosis factor-β (TNF-β) X X X X X Vascularendothelial growth factor (VEGF) X X X X X X B B-galactosidase X X XBruton's tyrosine kinase (BTK) X X X X CD40 ligand X X X X Complementprotein C1q (C1q) X X X Complement protein C3 (C3) X X X X Glucagon-likepeptide-1 (GLP-1) X X X B cell receptor μ□chain (IgM) X X X X XInterleukin-10 (IL-10) X X X Interleukin-11 (IL-11) X X X XInterleukin-13 (IL-13) X X X Interleukin-16 (IL-16) X X X X XInterleukin-18 (IL-18) X X X X Interleukin-1α (IL-1α) X X X X XInterleukin-1ra (IL-1ra) X X X X Interleukin-5 (IL-5) X X X XInterleukin-6 (IL-6) X X X X Interleukin-7 (IL-7) X X X X X Interferon-γ(INF-γ) X X X X Integrin α-11 X X X Janus kinase 3 protein tyrosinekinase (JAK3) X X X Lewis x/CD15 X X X Procathepsin W X X X Properdin XX X X X Sialyl Lewis x X X X C Complement protein C1s (C1s) X X XEotaxin X X X X Glucagon-like peptide-1 receptor (GLP-1 R) X X XIntegrin α-10 X X X X Monocyte chemotactic protein-3 (MCP-3) X X X X DComplement-1 esteras inhibitor (C1-INH) X X Complement protein C5 (C5) XX X X Tumour necrosis factor-α (TNF-α) X X X X E Interleukin-9 (IL-9) XX F Granulocyte-macrophage colony-stimulating factor (GM-CSF) X XInterleukin-2 (IL-2) X X X Leptin X X Lewis y X X Prostate specificantigen (PSA) X X Rantes X X G Angiomotin X Complement protein C4 (C4) XComplement Factor B (Factor B) X H Interleukin-1β (IL-1β) X Monocytechemotactic protein-4 (MCP-4) X TM peptide X

TABLE VII ROC-AUC values for differentiation between (A) pancreaticcancer, and (B) normal, chronic pancreatitis, and/or acute inflammatorypancreatitis ROC-AUC Biomarker signature 0.71 IL-7 0.69 Integrin α-100.76 IL-7 + Integrin α-10 + 1 Table IV B marker 0.79 IL-7 + Integrinα-10 + 2 Table IV B markers 0.80 IL-7 + Integrin α-10 + 3 Table IV Bmarkers 0.79 IL-7 + Integrin α-10 + 4 Table IV B markers 0.81 IL-7 +Integrin α-10 + 5 Table IV B markers 0.81 IL-7 + Integrin α-10 + 6 TableIV B markers 0.80 IL-7 + Integrin α-10 + 7 Table IV B markers 0.84IL-7 + Integrin α-10 + 8 Table IV B markers 0.79 IL-7 + Integrin α-10 +9 Table IV B markers 0.80 IL-7 + Integrin α-10 + 10 Table IV B markers0.79 IL-7 + Integrin α-10 + 11 Table IV B markers 0.76 IL-7 + Integrinα-10 + 12 Table IV B markers The core markers + 8 preferred markers gavethe best ROC-AUC value. The signature corresponds to (core marked inred): IL-7 + Integrin α-10 + BTK + C1q + IgM + IL-9 + Procathepsin W +properdin + TM peptide + b-galactosidase However, all markercombinations had substantial predictive power.

1. A method for determining the presence of pancreatic cancer in anindividual comprising or consisting of the steps of: a) providing asample to be tested from the individual; b) determining a biomarkersignature of the test sample by measuring the expression in the testsample of one or more biomarkers selected from the group defined inTable III; wherein the expression in the test sample of one or morebiomarkers selected from the group defined in Table III is indicative ofthe individual having pancreatic cancer; and wherein step (b) comprisesor consists of measuring the expression of one or more of the biomarkerslisted in Table IV(A) and/or Table IV(B).
 2. The method according toclaim 1 further comprising or consisting of the steps of: c) providing acontrol sample from an individual not afflicted with pancreatic cancer;d) determining a biomarker signature of the control sample by measuringthe expression in the control sample of the one or more biomarkersmeasured in step (b); wherein the presence of pancreatic cancer isidentified in the event that the expression in the test sample of theone or more biomarkers measured in step (b) is different from theexpression in the control sample of the one or more biomarkers measuredin step (d).
 3. The method according to claim 1 or 2 further comprisingor consisting of the steps of: e) providing a control sample from anindividual afflicted with pancreatic cancer; f) determining a biomarkersignature of the control sample by measuring the expression in thecontrol sample of the one or more biomarkers measured in step (b);wherein the presence of pancreatic cancer is identified in the eventthat the expression in the test sample of the one or more biomarkersmeasured in step (b) corresponds to the expression in the control sampleof the one or more biomarkers measured in step (f).
 4. The methodaccording to claim 1, 2 or 3, wherein step (b) comprises or consists ofmeasuring the expression of one or more of the biomarkers listed inTable IV(A), for example, at least 2 of the biomarkers listed in TableIV(A).
 5. The method according to any one of the preceding claims,wherein step (b) comprises or consists of measuring the expression ofinterleukin-7 (IL-7) and/or integrin alpha-10, for example, measuringthe expression of interleukin-7, measuring the expression of integrinalpha-10, or measuring the expression of interleukin-7 and integrinalpha-10.
 6. The method according to any one of the preceding claims,wherein step (b) comprises or consists of measuring the expression ofeach the biomarkers listed in Table IV(A).
 7. The method according toany one of the preceding claims, wherein step (b) comprises or consistsof measuring the expression of 1 or more of the biomarkers listed inTable IV(B), for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12of the biomarkers listed in Table IV(B).
 8. The method according to anyone of the preceding claims, wherein step (b) comprises or consists ofmeasuring the expression of all of the biomarkers listed in Table IV(B).9. The method according to any one of the preceding claims wherein step(b) comprises or consists of measuring the expression of 1 or morebiomarkers from the biomarkers listed in Table IV(C), for example atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40 or 41 of the biomarkers listed in Table IV(C).
 10. The methodaccording to any one of the preceding claims, wherein step (b) comprisesor consists of measuring the expression of all of the biomarkers listedin Table IV(C).
 11. The method according to any one of the precedingclaims wherein step (b) comprises or consists of measuring theexpression in the test sample of all of the biomarkers defined in TableIV.
 12. The method according to any one of the preceding claims, whereinthe method is for differentiating between pancreatic cancer (PaC) andany other disease state or states.
 13. The method according to claim 12,wherein step (b) comprises or consists of measuring the expression inthe test sample of 1 or more biomarkers from the biomarkers listed inTable V(A), for example at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 of thebiomarkers listed in Table V(A).
 14. The method according to claim 12 or13, wherein step (b) comprises or consists of measuring the expressionin the test sample of 1 or more biomarkers from the biomarkers listed inTable V(B), for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 of the biomarkers listed inTable V(B).
 15. The method according to claim 12, 13 or 14, wherein step(b) comprises or consists of measuring the expression in the test sampleof 1 or more biomarkers from the biomarkers listed in Table V(C), forexample at least 2, 3, 4 or 5 of the biomarkers listed in Table V(C).16. The method according to any one of claims 12 to 15, wherein step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(D), forexample at least 2 or 3 of the biomarkers listed in Table V(D).
 17. Themethod according to any one of claims 12 to 16, wherein step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(F), forexample at least 2, 3, 4, 5 or 6 of the biomarkers listed in Table V(F).18. The method according to any one of claims 12 to 17, wherein step (b)comprises or consists of measuring the expression in the test sample ofall of the biomarkers listed in Table V(A), Table V(B), Table V(C),Table V(D) and/or Table V(F).
 19. The method according to any one ofclaims 12 to 18, wherein the other disease state or states is chronicpancreatitis (ChP), acute inflammatory pancreatitis (AIP) and/or normal,for example, the other disease state or states may be chronicpancreatitis alone; acute inflammatory pancreatitis alone; chronicpancreatitis and acute inflammatory pancreatitis; chronic pancreatitisand normal; acute inflammatory pancreatitis and normal; or, chronicpancreatitis, acute inflammatory pancreatitis and normal.
 20. The methodaccording to any one of claims 1 to 11, wherein the method is fordifferentiating between pancreatic cancer and chronic pancreatitis(ChP).
 21. The method according to claim 20, wherein step (b) comprisesor consists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(A), for example atleast 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers listed in TableV(A).
 22. The method according to claim 20 or 21, wherein step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(C), forexample at least 2, 3, 4, or 5 of the biomarkers listed in Table V(C).23. The method according to claim 20, 21, or 22, wherein step (b)comprises or consists of measuring the expression in the test sample ofall of the biomarkers listed in Table V(A) and/or Table V(C).
 24. Themethod according to any one of claims 1 to 11, wherein the method is fordifferentiating between pancreatic cancer and acute inflammatorypancreatitis (AIP).
 25. The method according to claim 24, wherein step(b) comprises or consists of measuring the expression in the test sampleof 1 or more biomarkers from the biomarkers listed in Table V(A), forexample at least 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers listedin Table V(A).
 26. The method according to claim 24 or 25, wherein step(b) comprises or consists of measuring the expression in the test sampleof 1 or more biomarkers from the biomarkers listed in Table V(B), forexample at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23 or 24 of the biomarkers listed in Table V(B). 27.The method according to claim 24, 25 or 26, wherein step (b) comprisesor consists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(C), for example atleast 2, 3, 4 or 5 of the biomarkers listed in Table V(C).
 28. Themethod according to any one of claims 24 to 27, wherein step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(E).
 29. Themethod according to any one of claims 24 to 28, wherein step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(F), forexample at least 2, 3, 4, 5 or 6 of the biomarkers listed in Table V(F).30. The method according to any one of claims 24 to 29, wherein step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(H), forexample at least 2 or 3 of the biomarkers listed in Table V(H).
 31. Themethod according to any one of claims 24 to 30, wherein step (b)comprises or consists of or consists of measuring the expression in thetest sample of all of the biomarkers listed in Table V(A), Table V(B),Table V(C), Table V(E), Table V(F) and/or Table IV(H).
 32. The methodaccording to any one of claims 1 to 11, wherein the method is fordifferentiating between pancreatic cancer and normal individuals (N).33. The method according to claim 32, wherein step (b) comprises orconsists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(A), for example atleast 2, 3, 4, 5, 6, 7, 8, 9 or 10 of the biomarkers listed in TableV(A).
 34. The method according to claim 32 or 33, wherein step (b)comprises or consists of measuring the expression in the test sample of1 or more biomarkers from the biomarkers listed in Table V(B), forexample at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23 or 24 of the biomarkers listed in Table V(B). 35.The method according to claim 32, 33 or 34, wherein step (b) comprisesor consists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(D), for example atleast 2 or 3 of the biomarkers listed in Table V(D).
 36. The methodaccording to any one of claims 32 to 35, wherein step (b) comprises orconsists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(E).
 37. The methodaccording to any one of claims 32 to 36, wherein step (b) comprises orconsists of measuring the expression in the test sample of 1 or morebiomarkers from the biomarkers listed in Table V(G), for example atleast 2 or 3 of the biomarkers listed in Table V(G).
 38. The methodaccording to any one of claims 32 to 37, wherein step (b) comprises orconsists of measuring the expression in the test sample of all of thebiomarkers listed in Table V(A), Table V(B), Table V(D), Table V(E)and/or Table IV(G).
 39. The method according to any one of claims 2 to38, wherein the individual not afflicted with pancreatic cancer is notafflicted with pancreatic cancer (PaC), chronic pancreatitis (ChP) oracute inflammatory pancreatitis (AIP).
 40. The method according to claim39, wherein the individual not afflicted with pancreatic cancer is notafflicted with any pancreatic disease or condition.
 41. The methodaccording to claim 39 or 40, wherein the individual not afflicted withpancreatic cancer is not afflicted with any disease or condition. 42.The method according to claim 39, 40 or 41, wherein the individual notafflicted with pancreatic cancer is a healthy individual.
 43. The methodaccording to any one of claims 2 to 38, wherein the individual notafflicted with pancreatic cancer is afflicted with chronic pancreatitis.44. The method according to any one of claims 2 to 38, wherein theindividual not afflicted with pancreatic cancer is afflicted with acuteinflammatory pancreatitis.
 45. The method according to any one of thepreceding claims wherein the pancreatic cancer is selected from thegroup consisting of adenocarcinoma, adenosquamous carcinoma, signet ringcell carcinoma, hepatoid carcinoma, colloid carcinoma, undifferentiatedcarcinoma, and undifferentiated carcinomas with osteoclast-like giantcells.
 46. The method according to any one of the preceding claimswherein the pancreatic cancer is an adenocarcinoma.
 47. The methodaccording to any one of the preceding claims wherein step (b), (d)and/or step (f) is performed using a first binding agent capable ofbinding to the one or more biomarkers.
 48. The method according to claim47 wherein the first binding agent comprises or consists of an antibodyor an antigen-binding fragment thereof.
 49. The method according toclaim 48 wherein the antibody or antigen-binding fragment thereof is arecombinant antibody or antigen-binding fragment thereof.
 50. The methodaccording to claim 48 or 49 wherein the antibody or antigen-bindingfragment thereof is selected from the group consisting of: scFv; Fab; abinding domain of an immunoglobulin molecule.
 51. The method accordingto any one of claims 48 to 50 wherein the first binding agent isimmobilised on a surface.
 52. The method according to any one of claims1 to 25 wherein the one or more biomarkers in the test sample arelabelled with a detectable moiety.
 53. The method according to any oneof claims 2 to 25 wherein the one or more biomarkers in the controlsample(s) are labelled with a detectable moiety.
 54. The methodaccording to claim 52 or 53 wherein the detectable moiety is selectedfrom the group consisting of: a fluorescent moiety; a luminescentmoiety; a chemiluminescent moiety; a radioactive moiety; an enzymaticmoiety.
 55. The method according to claim 52 or 53 wherein thedetectable moiety is biotin.
 56. The method according to any one ofclaims 47 to 55 wherein step (b), (d) and/or step (f) is performed usingan assay comprising a second binding agent capable of binding to the oneor more biomarkers, the second binding agent comprising a detectablemoiety.
 57. The method according to any one of claim 56 wherein thesecond binding agent comprises or consists of an antibody or anantigen-binding fragment thereof.
 58. The method according to claim 57wherein the antibody or antigen-binding fragment thereof is arecombinant antibody or antigen-binding fragment thereof.
 59. The methodaccording to claim 57 or 58 wherein the antibody or antigen-bindingfragment thereof is selected from the group consisting of: scFv; Fab; abinding domain of an immunoglobulin molecule.
 60. The method accordingto any one of claims 56 to 59 wherein the detectable moiety is selectedfrom the group consisting of: a fluorescent moiety; a luminescentmoiety; a chemiluminescent moiety; a radioactive moiety; an enzymaticmoiety.
 61. The method according to claim 60 wherein the detectablemoiety is fluorescent moiety (for example an Alexa Fluor dye, e.g.Alexa647).
 62. The method according to any one of the preceding claimswherein the method comprises or consists of an ELISA (Enzyme LinkedImmunosorbent Assay).
 63. The method according to any one of thepreceding claims wherein step (b), (d) and/or step (f) is performedusing an array.
 64. The method according to claim 63 wherein the arrayis a bead-based array.
 65. The method according to claim 63 wherein thearray is a surface-based array.
 66. The method according to any one ofclaims 63 to 65 wherein the array is selected from the group consistingof: macroarray; microarray; nanoarray.
 67. The method according to anyone of the preceding claims wherein the method comprises: (v) labellingbiomarkers present in the sample with biotin; (vi) contacting thebiotin-labelled proteins with an array comprising a plurality of scFvimmobilised at discrete locations on its surface, the scFv havingspecificity for one or more of the proteins in Table III; (vii)contacting the immobilised scFv with a streptavidin conjugate comprisinga fluorescent dye; and (viii) detecting the presence of the dye atdiscrete locations on the array surface wherein the expression of thedye on the array surface is indicative of the expression of a biomarkerfrom Table III in the sample.
 68. The method according to any one ofclaims wherein, step (b), (d) and/or (f) comprises measuring theexpression of a nucleic acid molecule encoding the one or morebiomarkers.
 69. The method according to claim 68, wherein the nucleicacid molecule is a cDNA molecule or an mRNA molecule.
 70. The methodaccording to claim 68, wherein the nucleic acid molecule is an mRNAmolecule.
 71. The method according to claim 68, 69 or 70, whereinmeasuring the expression of the one or more biomarker(s) in step (b),(d) and/or (f) is performed using a method selected from the groupconsisting of Southern hybridisation, Northern hybridisation, polymerasechain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitativereal-time PCR (qRT-PCR), nanoarray, microarray, macroarray,autoradiography and in situ hybridisation.
 72. The method according toany one of claims 68-71, wherein measuring the expression of the one ormore biomarker(s) in step (b) is determined using a DNA microarray. 73.The method according to any one of claims 68 to 72, wherein measuringthe expression of the one or more biomarker(s) in step (b), (d) and/or(f) is performed using one or more binding moieties, each individuallycapable of binding selectively to a nucleic acid molecule encoding oneof the biomarkers identified in Table III.
 74. The method according toclaim 73, wherein the one or more binding moieties each comprise orconsist of a nucleic acid molecule.
 75. The method according to claim 74wherein, the one or more binding moieties each comprise or consist ofDNA, RNA, PNA, LNA, GNA, TNA or PMO.
 76. The method according to claim74 or 75, wherein the one or more binding moieties each comprise orconsist of DNA.
 77. The method according to any one of claims 74-76wherein the one or more binding moieties are 5 to 100 nucleotides inlength.
 78. The method according to any one of claims 74-76 wherein theone or more nucleic acid molecules are 15 to 35 nucleotides in length.79. The method according to any one of claims 74-78 wherein the bindingmoiety comprises a detectable moiety.
 80. The method according to claim79 wherein the detectable moiety is selected from the group consistingof: a fluorescent moiety; a luminescent moiety; a chemiluminescentmoiety; a radioactive moiety (for example, a radioactive atom); or anenzymatic moiety.
 81. The method according to claim 80 wherein thedetectable moiety comprises or consists of a radioactive atom.
 82. Themethod according to claim 81 wherein the radioactive atom is selectedfrom the group consisting of technetium-99m, iodine-123, iodine-125,iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17,phosphorus-32, sulphur-35, deuterium, tritium, rhenium-186, rhenium-188and yttrium-90.
 83. The method according to claim 80 wherein thedetectable moiety of the binding moiety is a fluorescent moiety.
 84. Themethod according to any one of the preceding claims wherein, the sampleprovided in step (b), (d) and/or (f) is selected from the groupconsisting of unfractionated blood, plasma, serum, tissue fluid,pancreatic tissue, pancreatic juice, bile and urine.
 85. The methodaccording to claim 84, wherein the sample provided in step (b), (d)and/or (f) is selected from the group consisting of unfractionatedblood, plasma and serum.
 86. The method according to claim 84 or 85,wherein the sample provided in step (b), (d) and/or (f) is plasma. 87.An array for determining the presence of pancreatic cancer in anindividual comprising one or more binding agent as defined in any one ofclaims 47 to
 61. 88. An array according to claim 87 wherein the one ormore binding agents is capable of binding to all of the proteins definedin Table III.
 89. Use of one or more biomarkers selected from the groupdefined in Table III as a diagnostic marker for determining the presenceof pancreatic cancer in an individual.
 90. The use according to claim 89wherein all of the proteins defined in Table III are used as adiagnostic marker for determining the presence of pancreatic cancer inan individual.
 91. A kit for determining the presence of pancreaticcancer comprising: C) one or more first binding agent as defined in anyone of claims 47 to 55 or an array according to claims 63 to 66 or claim87 or 88; D) instructions for performing the method as defined in anyone of claims 1 to 67 or 68 to
 86. 92. A kit according to claim 31further comprising a second binding agent as defined in any one ofclaims 56 to
 61. 93. A method or use for determining the presence ofpancreatic cancer in an individual substantially as described herein.94. An array or kit for determining the presence of pancreatic cancer inan individual substantially as described herein.